{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a05a8719-9869-4d79-ac2b-6029b4249afb",
   "metadata": {},
   "source": [
    "# Part A - Clark Center Survey\n",
    "1. preparation: loading programs, loading modules and api-key\n",
    "2. loading survey data.\n",
    "3. generate the ChatGPT opinion about these claims\n",
    "4. intermezzo: how stable are results over time and over different temperatures\n",
    "5. analyze the clark center data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e47ca468-c6f2-476b-aebc-6ce37671d23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read me before running the below code!\n",
    "\n",
    "# put your api-key here\n",
    "api_key=\"PUT YOUR API KEY HERE\"\n",
    "\n",
    "# note csv files with clark center data are in folders called clarkclaims2122 and clarkclaims (for2324)\n",
    "\n",
    "# if needed you might need to install some python modules\n",
    "#!pip install tabula-py\n",
    "#!pip install tabulate\n",
    "#!pip install --upgrade openai\n",
    "\n",
    "# modules to import\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from openai import OpenAI\n",
    "from IPython.display import display\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7834b5bf-b4da-44a1-9767-13da1fd838d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7     104\n",
       "1.2      58\n",
       "1.0      22\n",
       "0.2       8\n",
       "1         3\n",
       "0.8       2\n",
       "0.25      1\n",
       "1.        1\n",
       "0         1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some prep: what temperature does ChatGPT webversion use?\n",
    "# I ask ChatGPT'S api what is the value of the temperature parameter used by the web version of chatgpt4\n",
    "# the API has 1 as default\n",
    "# given the below answers i use 0.7,1 and 1.2 for 4o\n",
    "# i dont do different temperatures for 3.5 since 3.5 performs quite bad later on \n",
    "# and temperature in general doesnt make that much difference\n",
    "\n",
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "#claimtext= 'Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.'\n",
    "#claimtextinterpretation = 'When given the statement:' + claimtext + ', a person answers:' + claimanwswer + 'Do you think this answer means the person strongly disagrees with the statement, disagrees with the statement, is uncertain about the statement, agrees with the statement, or strongly agrees with the statement, or has no opinion about the statement. MAKE SURE your output is one of the categories stated only'\n",
    "\n",
    "n=200\n",
    "claimquestionanswergpt4o=[]\n",
    "\n",
    "for i in range(0,n):\n",
    "    claimtextquestion= 'what is the value of the temperature parameter used by the web version of chatgpt model 4o - give a number only'\n",
    "\n",
    "    completion = client.chat.completions.create(\n",
    "        model=\"gpt-4o\",\n",
    "      messages=[\n",
    "        {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "      ]\n",
    "    )\n",
    "    \n",
    "    claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "\n",
    "pd.Series(claimquestionanswergpt4o).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "00c755bf-0ae5-40a0-a659-c9309cc8deb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collecting the Clark Center Data\n",
    "# Note I do this in two steps - first 21-22 and then 23-24\n",
    "# This is for historical reasons: i started with 23-24 and then extended the dataset\n",
    "# but yes, code could be simplified to do this in one step :-)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7a9beb9f-6865-4728-91c9-704c1c8dd103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "A stable international tax system in which the major advanced economies collect a minimum rate on corporate income is achievable.\n",
      "An international tax system in which the major advanced economies set a minimum rate on corporate income is achievable.\n",
      "Despite repeated reforms of financial regulation (and macroprudential policies in some countries), there will always be occasional financial crises.\n",
      "Reforms of financial regulation since 2008 (and macroprudential policies in some countries) will not substantially reduce the probability of financial crises.\n",
      "Research on the nature and impact of bank runs has made it possible to limit substantially the wider economic damage from financial crises.\n",
      "Research on the nature and impact of bank runs has made it possible to limit the occurrence of financial crises and the economic damage they cause.\n",
      "Setting targets for schools to reduce obesity (e.g. by diverting financial resources to improve scho\n"
     ]
    }
   ],
   "source": [
    "# This is used for the 21-22 clark data\n",
    "# I gather data in dataframe based on csv files I downloaded from the Clark Center Website\n",
    "# these csv files are in a folder called clarkclaims2122\n",
    "# find claims that are available for both us and euro\n",
    "# then prepare data analysis by computing additional variables\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "xlfiles=glob.glob(\"clarkclaims2122/*\")\n",
    "\n",
    "allclaims=pd.DataFrame(columns=['Euro/US','Claim','Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer'])\n",
    "for j in xlfiles:\n",
    "    claim=pd.read_csv(j)\n",
    "    if 'Euro' in j:\n",
    "        eurous='Euro'\n",
    "    else:\n",
    "        eurous='US'\n",
    "    for k in [2,5,8]:\n",
    "        try:\n",
    "            claimorder12a=pd.DataFrame(columns=['Euro/US','Claim','Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer'])\n",
    "            claimorder12a.at[0,'Claim']=claim.iloc[:,k].name.replace(\"sugar, salt, and fat\",\"sugar, salt and fat\").replace(\"would be more welfare-improving\",\"would improve social welfare more\").replace(\"Debt Sustainability\\n\\n\\nDebt sustainability analysis\",\"Debt sustainability analysis\").replace(\"\\n\",\"\").replace(\"(a ‘wholesale CBDC').’\",\"\").replace(\"(a ‘wholesale CBDC').\",\"\").strip()\n",
    "            claimorder12a.at[0,'Euro/US']=eurous\n",
    "            for l in ['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer']:\n",
    "                claimorder12a.at[0,l]=np.sum(claim.iloc[:,k]==l)\n",
    "\n",
    "        except:\n",
    "            print('NA')\n",
    "        allclaims=pd.concat([allclaims,claimorder12a]).reset_index(drop=True)\n",
    "\n",
    "# restrict it to Unique claims that have no https links in them\n",
    "# and exclude the statement with a reference to next year\n",
    "uniqueclaims=[]\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    if 'https' not in i and 'Unnamed' not in i and 'raise global inflation over the next year' not in i:\n",
    "        uniqueclaims+=[i]\n",
    "\n",
    "# restrict to those which one are available for both US and Euro\n",
    "both=[]\n",
    "\n",
    "for i in uniqueclaims:\n",
    "    if np.sum(allclaims['Claim']==i)==2:\n",
    "        both+=[i]\n",
    "    else:\n",
    "        print(i)\n",
    "\n",
    "allclaims['Both Euro and US']=None\n",
    "for i in range(0,len(allclaims['Claim'])):\n",
    "    if allclaims['Claim'][i] in both:\n",
    "        allclaims['Both Euro and US'][i]=1\n",
    "    else:\n",
    "        allclaims['Both Euro and US'][i]=0\n",
    "allclaims = allclaims[allclaims['Both Euro and US']==1]\n",
    "allclaims = allclaims.reset_index(drop=True)\n",
    "\n",
    "# add row for sum of Euro and US\n",
    "\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    a=np.sum(allclaims[allclaims['Claim']==i])\n",
    "    a['Euro/US']='Together'\n",
    "    a['Claim']=i\n",
    "    allclaims=pd.concat([allclaims,a.to_frame().T]).reset_index(drop=True)\n",
    "allclaims=allclaims.drop(columns=['Both Euro and US'])    \n",
    "allclaims=allclaims.drop(columns=['Did Not Answer'])    \n",
    "\n",
    "# create shares of answers rather than absolute values\n",
    "allclaims['#Answered'] = (allclaims['Uncertain']+allclaims['No Opinion']+allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree'])\n",
    "allclaims['Have An Opinion'] = (100-(100*(allclaims['Uncertain']+allclaims['No Opinion'])/(allclaims['Uncertain']+allclaims['No Opinion']+allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree']))).astype(float).round(1)\n",
    "allclaims['Agree/Have An Opinion'] = (100*(allclaims['Strongly Agree'] +allclaims['Agree'])/(allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree'])).astype(float).round(1)\n",
    "allclaims['Strongly Agree'] = (100*allclaims['Strongly Agree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Agree'] = (100*allclaims['Agree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Strongly Disagree'] = (100*allclaims['Strongly Disagree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Disagree'] = (100*allclaims['Disagree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Uncertain'] = (100*allclaims['Uncertain']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['No Opinion'] = (100*allclaims['No Opinion']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Median'] =allclaims[['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion']].astype(float).idxmax(axis='columns')\n",
    "allclaims['MaxValue']=allclaims[['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion']].astype(float).max(axis='columns')\n",
    "\n",
    "allclaims=allclaims.sort_values(['Claim','Euro/US'])\n",
    "\n",
    "# set up summary dataframe based on excel docs\n",
    "allclaimssummary=pd.DataFrame(columns=['Claim',\"Euro Opinion\", \"US Opinion\", \"Euro + US Opinion\",\"Euro Agree\", \"US Agree\", \"Euro + US Agree\",\"Euro Median\", \"US Median\", \"Euro + US Median\",\"Euro MaxValue\", \"US MaxValue\", \"Euro + US MaxValue\"])\n",
    "\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    b=allclaims[allclaims['Claim']==i]\n",
    "    a=pd.DataFrame(columns=['Claim',\"Euro Opinion\", \"US Opinion\", \"Euro + US Opinion\",\"Euro Agree\", \"US Agree\", \"Euro + US Agree\",\"Euro Median\", \"US Median\", \"Euro + US Median\",\"Euro MaxValue\", \"US MaxValue\", \"Euro + US MaxValue\"])\n",
    "    a.at[0,'Claim']=i\n",
    "    a.at[0,\"Euro + US Opinion\"]=round(b[b['Euro/US']=='Together']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro + US Agree\"]=round(b[b['Euro/US']=='Together']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro Opinion\"]=round(b[b['Euro/US']=='Euro']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro Agree\"]=round(b[b['Euro/US']=='Euro']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"US Opinion\"]=round(b[b['Euro/US']=='US']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"US Agree\"]=round(b[b['Euro/US']=='US']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "\n",
    "    a.at[0,\"Euro Median\"]=b[b['Euro/US']=='Euro']['Median'].to_list()[0]\n",
    "    a.at[0,\"US Median\"]=b[b['Euro/US']=='US']['Median'].to_list()[0]\n",
    "    a.at[0,\"Euro + US Median\"]=b[b['Euro/US']=='Together']['Median'].to_list()[0]\n",
    "    \n",
    "    a.at[0,\"Euro MaxValue\"]=b[b['Euro/US']=='Euro']['MaxValue'].to_list()[0]\n",
    "    a.at[0,\"US MaxValue\"]=b[b['Euro/US']=='US']['MaxValue'].to_list()[0]\n",
    "    a.at[0,\"Euro + US MaxValue\"]=b[b['Euro/US']=='Together']['MaxValue'].to_list()[0]\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    allclaimssummary=pd.concat([allclaimssummary,a]).reset_index(drop=True)\n",
    "\n",
    "# adding the chance to get consensus\n",
    "a1=pd.DataFrame(columns=['Strongly Agree', 'Agree','Uncertain', 'No Opinion', 'Disagree', 'Strongly Disagree'])\n",
    "a1.at[0,'Strongly Agree']=1\n",
    "a1.at[0,'Agree']=2\n",
    "a1.at[0,'Uncertain']=3\n",
    "a1.at[0,'No Opinion']=3\n",
    "a1.at[0,'Disagree']=4\n",
    "a1.at[0,'Strongly Disagree']=5\n",
    "a1\n",
    "\n",
    "for j in range(0, len(allclaims)):\n",
    "\n",
    "    allclaims.at[j,'Chance Top Prof Choice']=allclaims[allclaims['Median'][j]][j]\n",
    "        \n",
    "    b=a1.loc[0]==a1[allclaims['Median'][j]].to_list()[0]+1\n",
    "    \n",
    "    try:\n",
    "        b1=b[b==True].idxmax()\n",
    "    except:\n",
    "        b1='to be removed'\n",
    "    \n",
    "    b=a1.loc[0]==a1[allclaims['Median'][j]].to_list()[0]-1\n",
    "    \n",
    "    try:\n",
    "        b2=b[b==True].idxmax()\n",
    "    except:\n",
    "        b2='to be removed'\n",
    "    b=[b1,b2]\n",
    "    c=[]\n",
    "    \n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=[i]\n",
    "    \n",
    "    for i in b:\n",
    "        if 'Uncertain' in i:\n",
    "            c+=['No Opinion']\n",
    "    for i in b:\n",
    "        if 'No Opinion' in i:\n",
    "            c+=['Uncertain']\n",
    "    allclaims.at[j,'Chance One Off Prof Choice']=np.sum(allclaims[c].loc[j])\n",
    "    allclaims.at[j,'Chance More than One Off Prof Choice']=100-allclaims.at[j,'Chance One Off Prof Choice']-allclaims.at[j,'Chance Top Prof Choice']\n",
    "allclaims\n",
    "\n",
    "allclaims2122=allclaims\n",
    "allclaimssummary2122=allclaimssummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4d7254d6-7c8d-41c4-94a2-8ae8254d6209",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "12\n",
      "13\n",
      "15\n",
      "16\n",
      "17\n",
      "18\n",
      "19\n",
      "20\n",
      "21\n",
      "22\n",
      "23\n",
      "24\n",
      "25\n",
      "26\n"
     ]
    }
   ],
   "source": [
    "# now ask chatgpt the 21-22 questions\n",
    "# to see whether temperature makes a difference we try the three main temperature values of 1 (default), 0.7 and 1.2 \n",
    "# we also try 3.5\n",
    "# and we add one with an economics persona (and default temp of 1]\n",
    "# we do each question 200 times to get the distribution\n",
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "#claimtext= 'Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.'\n",
    "#claimtextinterpretation = 'When given the statement:' + claimtext + ', a person answers:' + claimanwswer + 'Do you think this answer means the person strongly disagrees with the statement, disagrees with the statement, is uncertain about the statement, agrees with the statement, or strongly agrees with the statement, or has no opinion about the statement. MAKE SURE your output is one of the categories stated only'\n",
    "\n",
    "n=200\n",
    "\n",
    "claimtexts=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4otemp12=[]\n",
    "claimquestionanswergpt4otemp07=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "claimquestionanswergpt35=[]\n",
    "\n",
    "for j in range(0,len(allclaimssummary['Claim'])):\n",
    "    print(j)\n",
    "    claimtext=allclaimssummary['Claim'][j]\n",
    "    claimtexts+=[claimtext]\n",
    "    for i in range(0,n):\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",temperature=1.2,\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4otemp12+=[completion.choices[0].message.content]\n",
    "\n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",temperature=0.7,\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4otemp07+=[completion.choices[0].message.content]\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "#dfecon['claimanswer']=claimanswer\n",
    "#dfecon['claiminterpretation']=claiminterpretation\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "df['claimquestionanswergpt4otemp12']=claimquestionanswergpt4otemp12\n",
    "df['claimquestionanswergpt4otemp07']=claimquestionanswergpt4otemp07\n",
    "\n",
    "df['claimtext']=np.repeat(claimtexts,n)\n",
    "\n",
    "# this is for 2122 data\n",
    "df.to_pickle('claimsClarkCenter2122090824')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8fcfa705-5ef8-4e04-9c70-00ebcf41fa6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "NA\n",
      "Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.\n",
      "Antitrust investigations of the dominant firms in artificial intelligence are likely to promote greater competition and innovation in AI.\n",
      "Potential harms from artificial intelligence are better assessed by market deployment rather than seeking to slow the pace of AI research and implementation.\n",
      "Seeking to slow the pace of artificial intelligence use and implementation would be a more effective means of assessing potential harms from the technologies than market deployment and ex post assessment.\n",
      "The proposed US tariffs on Chinese EVs would lead to measurably higher prices of EVs in the US.\n",
      "US antitrust investigations of the dominant firms in artificial intelligence are warranted by the need to foster competition and innovation in the technologies.\n",
      "Unless the EU matches the proposed US tariffs on Chinese EVs, there would be measurably lower employment in Europe's automotive industry over the next five years.\n"
     ]
    }
   ],
   "source": [
    "# this is for clark 23-24 data\n",
    "# I gather data in dataframe based on csv files I downloaded from the Clark Center Website\n",
    "# these csv files are in a folder called clarkclaims\n",
    "# find claims that are available for both us and euro\n",
    "# then prepare data analysis by computing additional variables\n",
    "import glob\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "xlfiles=glob.glob(\"clarkclaims/*\")\n",
    "\n",
    "allclaims=pd.DataFrame(columns=['Euro/US','Claim','Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer'])\n",
    "for j in xlfiles:\n",
    "    claim=pd.read_csv(j)\n",
    "    if 'Euro' in j:\n",
    "        eurous='Euro'\n",
    "    else:\n",
    "        eurous='US'\n",
    "    for k in [2,5,8]:\n",
    "        try:\n",
    "            claimorder12a=pd.DataFrame(columns=['Euro/US','Claim','Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer'])\n",
    "            claimorder12a.at[0,'Claim']=claim.iloc[:,k].name.replace(\"Policy Responses to Recent Bank Failures\",\"\").replace(\"Debt Sustainability\\n\\n \\nDebt sustainability analysis\",\"Debt sustainability analysis\").replace(\"Debt Sustainability\\n\\n\\nDebt sustainability analysis\",\"Debt sustainability analysis\").replace(\"\\n\",\"\").replace(\"AI and Market Power \",\"\").replace(\"AI and the Labor Market \",\"\").strip()\n",
    "            claimorder12a.at[0,'Euro/US']=eurous\n",
    "            for l in ['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion', 'Did Not Answer']:\n",
    "                claimorder12a.at[0,l]=np.sum(claim.iloc[:,k]==l)\n",
    "        \n",
    "        except:\n",
    "            print('NA')\n",
    "        allclaims=pd.concat([allclaims,claimorder12a]).reset_index(drop=True)\n",
    "\n",
    "# restrict it to Unique claims that have no https links in them\n",
    "uniqueclaims=[]\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    if 'https' not in i:\n",
    "        uniqueclaims+=[i]\n",
    "\n",
    "# restrict to those which one are available for both US and Euro\n",
    "both=[]\n",
    "\n",
    "for i in uniqueclaims:\n",
    "    if np.sum(allclaims['Claim']==i)==2:\n",
    "        both+=[i]\n",
    "    else:\n",
    "        print(i)\n",
    "\n",
    "allclaims['Both Euro and US']=None\n",
    "for i in range(0,len(allclaims['Claim'])):\n",
    "    if allclaims['Claim'][i] in both:\n",
    "        allclaims['Both Euro and US'][i]=1\n",
    "    else:\n",
    "        allclaims['Both Euro and US'][i]=0\n",
    "allclaims = allclaims[allclaims['Both Euro and US']==1]\n",
    "allclaims = allclaims.reset_index(drop=True)\n",
    "\n",
    "# add row for sum of Euro and US\n",
    "\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    a=np.sum(allclaims[allclaims['Claim']==i])\n",
    "    a['Euro/US']='Together'\n",
    "    a['Claim']=i\n",
    "    allclaims=pd.concat([allclaims,a.to_frame().T]).reset_index(drop=True)\n",
    "allclaims=allclaims.drop(columns=['Both Euro and US'])    \n",
    "allclaims=allclaims.drop(columns=['Did Not Answer'])    \n",
    "\n",
    "# create shares of answers rather than absolute values\n",
    "allclaims['#Answered'] = (allclaims['Uncertain']+allclaims['No Opinion']+allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree'])\n",
    "allclaims['Have An Opinion'] = (100-(100*(allclaims['Uncertain']+allclaims['No Opinion'])/(allclaims['Uncertain']+allclaims['No Opinion']+allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree']))).astype(float).round(1)\n",
    "allclaims['Agree/Have An Opinion'] = (100*(allclaims['Strongly Agree'] +allclaims['Agree'])/(allclaims['Strongly Agree'] +allclaims['Agree']+allclaims['Strongly Disagree'] +allclaims['Disagree'])).astype(float).round(1)\n",
    "allclaims['Strongly Agree'] = (100*allclaims['Strongly Agree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Agree'] = (100*allclaims['Agree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Strongly Disagree'] = (100*allclaims['Strongly Disagree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Disagree'] = (100*allclaims['Disagree']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Uncertain'] = (100*allclaims['Uncertain']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['No Opinion'] = (100*allclaims['No Opinion']/allclaims['#Answered']).astype(float).round(1)\n",
    "allclaims['Median'] =allclaims[['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion']].astype(float).idxmax(axis='columns')\n",
    "allclaims['MaxValue']=allclaims[['Strongly Agree', 'Agree', 'Disagree', 'Strongly Disagree','Uncertain', 'No Opinion']].astype(float).max(axis='columns')\n",
    "\n",
    "allclaims=allclaims.sort_values(['Claim','Euro/US'])\n",
    "\n",
    "\n",
    "# set up summary dataframe based on excel docs\n",
    "allclaimssummary=pd.DataFrame(columns=['Claim',\"Euro Opinion\", \"US Opinion\", \"Euro + US Opinion\",\"Euro Agree\", \"US Agree\", \"Euro + US Agree\",\"Euro Median\", \"US Median\", \"Euro + US Median\",\"Euro MaxValue\", \"US MaxValue\", \"Euro + US MaxValue\"])\n",
    "\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    b=allclaims[allclaims['Claim']==i]\n",
    "    a=pd.DataFrame(columns=['Claim',\"Euro Opinion\", \"US Opinion\", \"Euro + US Opinion\",\"Euro Agree\", \"US Agree\", \"Euro + US Agree\",\"Euro Median\", \"US Median\", \"Euro + US Median\",\"Euro MaxValue\", \"US MaxValue\", \"Euro + US MaxValue\"])\n",
    "    a.at[0,'Claim']=i\n",
    "    a.at[0,\"Euro + US Opinion\"]=round(b[b['Euro/US']=='Together']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro + US Agree\"]=round(b[b['Euro/US']=='Together']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro Opinion\"]=round(b[b['Euro/US']=='Euro']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"Euro Agree\"]=round(b[b['Euro/US']=='Euro']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"US Opinion\"]=round(b[b['Euro/US']=='US']['Have An Opinion'].to_list()[0],1)\n",
    "    a.at[0,\"US Agree\"]=round(b[b['Euro/US']=='US']['Agree/Have An Opinion'].to_list()[0],1)\n",
    "\n",
    "    a.at[0,\"Euro Median\"]=b[b['Euro/US']=='Euro']['Median'].to_list()[0]\n",
    "    a.at[0,\"US Median\"]=b[b['Euro/US']=='US']['Median'].to_list()[0]\n",
    "    a.at[0,\"Euro + US Median\"]=b[b['Euro/US']=='Together']['Median'].to_list()[0]\n",
    "    \n",
    "    a.at[0,\"Euro MaxValue\"]=b[b['Euro/US']=='Euro']['MaxValue'].to_list()[0]\n",
    "    a.at[0,\"US MaxValue\"]=b[b['Euro/US']=='US']['MaxValue'].to_list()[0]\n",
    "    a.at[0,\"Euro + US MaxValue\"]=b[b['Euro/US']=='Together']['MaxValue'].to_list()[0]\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    allclaimssummary=pd.concat([allclaimssummary,a]).reset_index(drop=True)\n",
    "\n",
    "# adding the chance to get consensus\n",
    "\n",
    "for j in range(0, len(allclaims)):\n",
    "\n",
    "    allclaims.at[j,'Chance Top Prof Choice']=allclaims[allclaims['Median'][j]][j]\n",
    "        \n",
    "    b=a1.loc[0]==a1[allclaims['Median'][j]].to_list()[0]+1\n",
    "    \n",
    "    try:\n",
    "        b1=b[b==True].idxmax()\n",
    "    except:\n",
    "        b1='to be removed'\n",
    "    \n",
    "    b=a1.loc[0]==a1[allclaims['Median'][j]].to_list()[0]-1\n",
    "    \n",
    "    try:\n",
    "        b2=b[b==True].idxmax()\n",
    "    except:\n",
    "        b2='to be removed'\n",
    "    b=[b1,b2]\n",
    "    c=[]\n",
    "    \n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=[i]\n",
    "    \n",
    "    for i in b:\n",
    "        if 'Uncertain' in i:\n",
    "            c+=['No Opinion']\n",
    "    for i in b:\n",
    "        if 'No Opinion' in i:\n",
    "            c+=['Uncertain']\n",
    "    allclaims.at[j,'Chance One Off Prof Choice']=np.sum(allclaims[c].loc[j])\n",
    "    allclaims.at[j,'Chance More than One Off Prof Choice']=100-allclaims.at[j,'Chance One Off Prof Choice']-allclaims.at[j,'Chance Top Prof Choice']\n",
    "allclaims\n",
    "\n",
    "allclaims2324=allclaims\n",
    "allclaimssummary2324=allclaimssummary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fca681d-3738-41de-aaca-7c682e736f7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# now ask chatgpt the 23/24 questions\n",
    "# to see whether temperature makes a difference we try the three main temperature values of 1 (default), 0.7 and 1.2 \n",
    "# we also try 3.5\n",
    "# and we add one with an economics persona (and default temp of 1]\n",
    "# we do each question 200 times to get the distribution\n",
    "# this code can be use for 2122 and 2324\n",
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "#claimtext= 'Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.'\n",
    "#claimtextinterpretation = 'When given the statement:' + claimtext + ', a person answers:' + claimanwswer + 'Do you think this answer means the person strongly disagrees with the statement, disagrees with the statement, is uncertain about the statement, agrees with the statement, or strongly agrees with the statement, or has no opinion about the statement. MAKE SURE your output is one of the categories stated only'\n",
    "\n",
    "n=200\n",
    "\n",
    "claimtexts=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4otemp12=[]\n",
    "claimquestionanswergpt4otemp07=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "claimquestionanswergpt35=[]\n",
    "\n",
    "for j in range(0,len(allclaimssummary['Claim'])):\n",
    "    print(j)\n",
    "    claimtext=allclaimssummary['Claim'][j]\n",
    "    claimtexts+=[claimtext]\n",
    "    for i in range(0,n):\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",temperature=1.2,\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4otemp12+=[completion.choices[0].message.content]\n",
    "\n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",temperature=0.7,\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4otemp07+=[completion.choices[0].message.content]\n",
    "\n",
    "\n",
    "\n",
    "        \n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "#dfecon['claimanswer']=claimanswer\n",
    "#dfecon['claiminterpretation']=claiminterpretation\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "df['claimquestionanswergpt4otemp12']=claimquestionanswergpt4otemp12\n",
    "df['claimquestionanswergpt4otemp07']=claimquestionanswergpt4otemp07\n",
    "\n",
    "df['claimtext']=np.repeat(claimtexts,n)\n",
    "\n",
    "df.to_pickle('claimsClarkCenter2324090824')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "710df581-6f7f-4ab9-a9ad-23fe1a911dea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claimquestionanswergpt4oclean\n",
      "agree                7344\n",
      "uncertain            3363\n",
      "strongly agree        836\n",
      "disagree              438\n",
      "strongly disagree      16\n",
      "no opinion              3\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4otemp12clean\n",
      "agree                7162\n",
      "uncertain            3343\n",
      "strongly agree       1012\n",
      "disagree              427\n",
      "strongly disagree      34\n",
      "no opinion             20\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4otemp07clean\n",
      "agree                7471\n",
      "uncertain            3367\n",
      "strongly agree        714\n",
      "disagree              439\n",
      "strongly disagree       6\n",
      "no opinion              3\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4oprofclean\n",
      "agree                6583\n",
      "uncertain            3408\n",
      "strongly agree       1480\n",
      "disagree              500\n",
      "strongly disagree      26\n",
      "no opinion              3\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt35clean\n",
      "agree                5068\n",
      "strongly agree       3962\n",
      "strongly disagree    1949\n",
      "disagree              710\n",
      "uncertain             310\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_408466/1688303764.py:94: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:98: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:94: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:94: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:95: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp12 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:98: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:94: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:98: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1688303764.py:96: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n"
     ]
    }
   ],
   "source": [
    "#intermezzo: are results stable over time\n",
    "# we had downloaded data a week earlier - let's load those and then compare with new results to see stability\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df1=pd.read_pickle('claimsClarkCentergpt4otemperature2122')\n",
    "df2=pd.read_pickle('claimsClarkCentergpt4otemperature')\n",
    "df=pd.concat([df1,df2], axis=0).reset_index(drop=True)\n",
    "\n",
    "allclaims=pd.concat([allclaims2122,allclaims2324], axis=0).reset_index(drop=True)\n",
    "allclaimssummary=pd.concat([allclaimssummary2122,allclaimssummary2324], axis=0).reset_index(drop=True)\n",
    "allclaimssummary=allclaimssummary.sort_values('Claim').reset_index(drop=True)\n",
    "# analysis of data\n",
    "n=200\n",
    "\n",
    "# we start by cleaning the answers as ChatGPT does not always gives just the category as an answer (despite being asked to do so).\n",
    "# so we clean the answers by extracting the categorical answers from the raw answers\n",
    "\n",
    "df['claimquestionanswergpt4oclean']=None\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('agree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oclean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4otemp12clean']=None\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('agree', case=False) & (df['claimquestionanswergpt4otemp12'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4otemp12'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12']=='agere']='agree'\n",
    "print(df['claimquestionanswergpt4otemp12clean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4otemp07clean']=None\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('agree', case=False) & (df['claimquestionanswergpt4otemp07'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4otemp07'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4otemp07clean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean']=None\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('agree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oprofclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt35clean']=None\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('disagree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('agree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt35clean'].value_counts())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# now create overview stats that give \n",
    "#share that have an opinion\n",
    "# share positive among those having anm opinion [sometimes there is no observation with an opinion so that gives NAN]\n",
    "# mode of opinion\n",
    "allclaimssummaryGPTold=pd.DataFrame(columns=['Claim',\"GPT4o Opinion\",\"GPT4otemp12 Opinion\",\"GPT4otemp07 Opinion\", \"GPT3.5 Opinion\", \"GPT4oProf Opinion\",\"GPT4o Agree\",\"GPT4otemp12 Agree\",\"GPT4otemp07 Agree\", \"GPT3.5 Agree\", \"GPT4oProf Agree\",\"GPT4o Median\",\"GPT4otemp12 Median\",\"GPT4otemp07 Median\", \"GPT3.5 Median\", \"GPT4oProf Median\",\"GPT4o MaxValue\",\"GPT4otemp12 MaxValue\",\"GPT4otemp07 MaxValue\", \"GPT3.5 MaxValue\", \"GPT4oProf MaxValue\"])\n",
    "\n",
    "for i in np.unique(df['claimtext']):\n",
    "    b=df[df['claimtext']==i]\n",
    "    a=pd.DataFrame(columns=['Claim',\"GPT4o Opinion\",\"GPT4otemp12 Opinion\",\"GPT4otemp07 Opinion\", \"GPT3.5 Opinion\", \"GPT4oProf Opinion\",\"GPT4o Agree\",\"GPT4otemp12 Agree\",\"GPT4otemp07 Agree\", \"GPT3.5 Agree\", \"GPT4oProf Agree\",\"GPT4o Median\",\"GPT4otemp12 Median\",\"GPT4otemp07 Median\", \"GPT3.5 Median\", \"GPT4oProf Median\",\"GPT4o MaxValue\",\"GPT4otemp12 MaxValue\",\"GPT4otemp07 MaxValue\", \"GPT3.5 MaxValue\", \"GPT4oProf MaxValue\"])\n",
    "    a.at[0,'Claim']=i\n",
    "    a.at[0,\"GPT4o Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4oclean']=='no opinion')+np.sum(b['claimquestionanswergpt4oclean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4otemp12 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='no opinion')+np.sum(b['claimquestionanswergpt4otemp12clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4otemp07 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='no opinion')+np.sum(b['claimquestionanswergpt4otemp07clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT3.5 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt35clean']=='no opinion')+np.sum(b['claimquestionanswergpt35clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4oProf Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4oprofclean']=='no opinion')+np.sum(b['claimquestionanswergpt4oprofclean']=='uncertain'))/n) \n",
    "    \n",
    "    a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
    "    a.at[0,\"GPT4otemp12 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='disagree'))\n",
    "    a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
    "    a.at[0,\"GPT3.5 Agree\"]=100*(np.sum(b['claimquestionanswergpt35clean']=='strongly agree')+np.sum(b['claimquestionanswergpt35clean']=='agree'))/(np.sum(b['claimquestionanswergpt35clean']=='strongly agree')+np.sum(b['claimquestionanswergpt35clean']=='agree')+np.sum(b['claimquestionanswergpt35clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt35clean']=='disagree'))\n",
    "    a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
    "\n",
    "    a.at[0,\"GPT4o Median\"]=b['claimquestionanswergpt4oclean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4otemp12 Median\"]=b['claimquestionanswergpt4otemp12clean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4otemp07 Median\"]=b['claimquestionanswergpt4otemp07clean'].value_counts().idxmax()    \n",
    "    a.at[0,\"GPT3.5 Median\"]=b['claimquestionanswergpt35clean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4oProf Median\"]=b['claimquestionanswergpt4oprofclean'].value_counts().idxmax()\n",
    "\n",
    "    a.at[0,\"GPT4o MaxValue\"]=b['claimquestionanswergpt4oclean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4otemp12 MaxValue\"]=b['claimquestionanswergpt4otemp12clean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4otemp07 MaxValue\"]=b['claimquestionanswergpt4otemp07clean'].value_counts().max()/(n/100) \n",
    "    a.at[0,\"GPT3.5 MaxValue\"]=b['claimquestionanswergpt35clean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4oProf MaxValue\"]=b['claimquestionanswergpt4oprofclean'].value_counts().max()/(n/100)\n",
    "\n",
    "    \n",
    "    allclaimssummaryGPTold=pd.concat([allclaimssummaryGPTold,a]).reset_index(drop=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e8c4293a-08d8-4d43-b423-7b5bf49a94e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claimquestionanswergpt4oclean\n",
      "agree                7246\n",
      "uncertain            3541\n",
      "strongly agree        802\n",
      "disagree              383\n",
      "strongly disagree      24\n",
      "no opinion              4\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4otemp12clean\n",
      "agree                7175\n",
      "uncertain            3453\n",
      "strongly agree        941\n",
      "disagree              380\n",
      "strongly disagree      39\n",
      "no opinion             12\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4otemp07clean\n",
      "agree                7277\n",
      "uncertain            3612\n",
      "strongly agree        702\n",
      "disagree              399\n",
      "strongly disagree       7\n",
      "no opinion              3\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4oprofclean\n",
      "agree                6684\n",
      "uncertain            3261\n",
      "strongly agree       1464\n",
      "disagree              550\n",
      "strongly disagree      40\n",
      "no opinion              1\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt35clean\n",
      "agree                5072\n",
      "strongly agree       4038\n",
      "strongly disagree    1913\n",
      "disagree              700\n",
      "uncertain             277\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:80: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:81: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:82: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:83: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_408466/1690012669.py:84: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   claimquestionanswergpt4o1  claimquestionanswergpt4otemp121  \\\n",
      "strongly agree                           NaN                              0.5   \n",
      "agree                                  100.0                             99.5   \n",
      "uncertain                                NaN                              NaN   \n",
      "disagree                                 NaN                              NaN   \n",
      "strongly disagree                        NaN                              NaN   \n",
      "no opinion                               NaN                              NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp071  \\\n",
      "strongly agree                                 NaN   \n",
      "agree                                        100.0   \n",
      "uncertain                                      NaN   \n",
      "disagree                                       NaN   \n",
      "strongly disagree                              NaN   \n",
      "no opinion                                     NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof1  claimquestionanswergpt351  \\\n",
      "strongly agree                               NaN                       59.5   \n",
      "agree                                       59.5                       40.5   \n",
      "uncertain                                   40.5                        NaN   \n",
      "disagree                                     NaN                        NaN   \n",
      "strongly disagree                            NaN                        NaN   \n",
      "no opinion                                   NaN                        NaN   \n",
      "\n",
      "                   claimquestionanswergpt4o2  claimquestionanswergpt4otemp122  \\\n",
      "strongly agree                           2.5                              2.5   \n",
      "agree                                   96.0                             95.0   \n",
      "uncertain                                1.5                              2.5   \n",
      "disagree                                 NaN                              NaN   \n",
      "strongly disagree                        NaN                              NaN   \n",
      "no opinion                               NaN                              NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp072  \\\n",
      "strongly agree                                 NaN   \n",
      "agree                                        100.0   \n",
      "uncertain                                      NaN   \n",
      "disagree                                       NaN   \n",
      "strongly disagree                              NaN   \n",
      "no opinion                                     NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof2  claimquestionanswergpt352  \\\n",
      "strongly agree                               NaN                       48.5   \n",
      "agree                                       99.0                       48.5   \n",
      "uncertain                                    1.0                        1.0   \n",
      "disagree                                     NaN                        1.0   \n",
      "strongly disagree                            NaN                        1.0   \n",
      "no opinion                                   NaN                        NaN   \n",
      "\n",
      "                   ...  claimquestionanswergpt4o59  \\\n",
      "strongly agree     ...                         NaN   \n",
      "agree              ...                        17.5   \n",
      "uncertain          ...                        81.0   \n",
      "disagree           ...                         1.0   \n",
      "strongly disagree  ...                         0.5   \n",
      "no opinion         ...                         NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp1259  \\\n",
      "strongly agree                                  0.5   \n",
      "agree                                          27.0   \n",
      "uncertain                                      72.0   \n",
      "disagree                                        0.5   \n",
      "strongly disagree                               NaN   \n",
      "no opinion                                      NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp0759  \\\n",
      "strongly agree                                  NaN   \n",
      "agree                                          12.5   \n",
      "uncertain                                      87.0   \n",
      "disagree                                        0.5   \n",
      "strongly disagree                               NaN   \n",
      "no opinion                                      NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof59  claimquestionanswergpt3559  \\\n",
      "strongly agree                                2.5                         7.0   \n",
      "agree                                        73.0                        20.5   \n",
      "uncertain                                    22.5                        52.5   \n",
      "disagree                                      2.0                         3.5   \n",
      "strongly disagree                             NaN                        16.5   \n",
      "no opinion                                    NaN                         NaN   \n",
      "\n",
      "                   claimquestionanswergpt4o60  \\\n",
      "strongly agree                            1.5   \n",
      "agree                                    98.5   \n",
      "uncertain                                 NaN   \n",
      "disagree                                  NaN   \n",
      "strongly disagree                         NaN   \n",
      "no opinion                                NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp1260  \\\n",
      "strongly agree                                  3.5   \n",
      "agree                                          96.5   \n",
      "uncertain                                       NaN   \n",
      "disagree                                        NaN   \n",
      "strongly disagree                               NaN   \n",
      "no opinion                                      NaN   \n",
      "\n",
      "                   claimquestionanswergpt4otemp0760  \\\n",
      "strongly agree                                  0.5   \n",
      "agree                                          99.5   \n",
      "uncertain                                       NaN   \n",
      "disagree                                        NaN   \n",
      "strongly disagree                               NaN   \n",
      "no opinion                                      NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof60  claimquestionanswergpt3560  \n",
      "strongly agree                                3.5                         8.0  \n",
      "agree                                        96.5                        23.0  \n",
      "uncertain                                     NaN                         NaN  \n",
      "disagree                                      NaN                         8.5  \n",
      "strongly disagree                             NaN                        60.5  \n",
      "no opinion                                    NaN                         NaN  \n",
      "\n",
      "[6 rows x 300 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_408466/1690012669.py:106: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:104: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:106: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:104: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:105: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp12 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:106: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:108: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:104: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:105: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp12 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:106: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:104: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
      "/tmp/ipykernel_408466/1690012669.py:106: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df1=pd.read_pickle('claimsClarkCenter2122090824')\n",
    "df2=pd.read_pickle('claimsClarkCenter2324090824')\n",
    "df=pd.concat([df1,df2], axis=0).reset_index(drop=True)\n",
    "\n",
    "allclaims=pd.concat([allclaims2122,allclaims2324], axis=0).reset_index(drop=True)\n",
    "allclaimssummary=pd.concat([allclaimssummary2122,allclaimssummary2324], axis=0).reset_index(drop=True)\n",
    "allclaimssummary=allclaimssummary.sort_values('Claim').reset_index(drop=True)\n",
    "# analysis of data\n",
    "n=200\n",
    "\n",
    "# we start by cleaning the answers as ChatGPT does not always gives just the category as an answer (despite being asked to do so).\n",
    "# so we clean the answers by extracting the categorical answers from the raw answers\n",
    "\n",
    "df['claimquestionanswergpt4oclean']=None\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('agree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oclean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4otemp12clean']=None\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12'].str.contains('agree', case=False) & (df['claimquestionanswergpt4otemp12'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4otemp12'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4otemp12'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4otemp12clean'].loc[df['claimquestionanswergpt4otemp12']=='agere']='agree'\n",
    "print(df['claimquestionanswergpt4otemp12clean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4otemp07clean']=None\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07'].str.contains('agree', case=False) & (df['claimquestionanswergpt4otemp07'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4otemp07'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4otemp07'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4otemp07clean'].loc[df['claimquestionanswergpt4otemp07']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4otemp07clean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean']=None\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('agree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oprofclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt35clean']=None\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('disagree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('agree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt35clean'].value_counts())\n",
    "\n",
    "\n",
    "\n",
    "# overview by question, giving everything in %\n",
    "fixedform=pd.DataFrame(index=['strongly agree','agree', 'uncertain', 'disagree', 'strongly disagree','no opinion'])\n",
    "ct=0\n",
    "for i in np.unique(allclaims['Claim']):\n",
    "    ct=ct+1\n",
    "    dfs=df[df['claimtext']==i]    \n",
    "    fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt4otemp12'+str(ct)]=dfs['claimquestionanswergpt4otemp12clean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt4otemp07'+str(ct)]=dfs['claimquestionanswergpt4otemp07clean'].value_counts()/(n/100)    \n",
    "    fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
    "\n",
    "print(fixedform)\n",
    "\n",
    "# now create overview stats that give \n",
    "#share that have an opinion\n",
    "# share positive among those having anm opinion [sometimes there is no observation with an opinion so that gives NAN]\n",
    "# mode of opinion\n",
    "allclaimssummaryGPT=pd.DataFrame(columns=['Claim',\"GPT4o Opinion\",\"GPT4otemp12 Opinion\",\"GPT4otemp07 Opinion\", \"GPT3.5 Opinion\", \"GPT4oProf Opinion\",\"GPT4o Agree\",\"GPT4otemp12 Agree\",\"GPT4otemp07 Agree\", \"GPT3.5 Agree\", \"GPT4oProf Agree\",\"GPT4o Median\",\"GPT4otemp12 Median\",\"GPT4otemp07 Median\", \"GPT3.5 Median\", \"GPT4oProf Median\",\"GPT4o MaxValue\",\"GPT4otemp12 MaxValue\",\"GPT4otemp07 MaxValue\", \"GPT3.5 MaxValue\", \"GPT4oProf MaxValue\"])\n",
    "\n",
    "for i in np.unique(df['claimtext']):\n",
    "    b=df[df['claimtext']==i]\n",
    "    a=pd.DataFrame(columns=['Claim',\"GPT4o Opinion\",\"GPT4otemp12 Opinion\",\"GPT4otemp07 Opinion\", \"GPT3.5 Opinion\", \"GPT4oProf Opinion\",\"GPT4o Agree\",\"GPT4otemp12 Agree\",\"GPT4otemp07 Agree\", \"GPT3.5 Agree\", \"GPT4oProf Agree\",\"GPT4o Median\",\"GPT4otemp12 Median\",\"GPT4otemp07 Median\", \"GPT3.5 Median\", \"GPT4oProf Median\",\"GPT4o MaxValue\",\"GPT4otemp12 MaxValue\",\"GPT4otemp07 MaxValue\", \"GPT3.5 MaxValue\", \"GPT4oProf MaxValue\"])\n",
    "    a.at[0,'Claim']=i\n",
    "    a.at[0,\"GPT4o Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4oclean']=='no opinion')+np.sum(b['claimquestionanswergpt4oclean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4otemp12 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='no opinion')+np.sum(b['claimquestionanswergpt4otemp12clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4otemp07 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='no opinion')+np.sum(b['claimquestionanswergpt4otemp07clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT3.5 Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt35clean']=='no opinion')+np.sum(b['claimquestionanswergpt35clean']=='uncertain'))/n)\n",
    "    a.at[0,\"GPT4oProf Opinion\"]=100-(100*(np.sum(b['claimquestionanswergpt4oprofclean']=='no opinion')+np.sum(b['claimquestionanswergpt4oprofclean']=='uncertain'))/n) \n",
    "    \n",
    "    a.at[0,\"GPT4o Agree\"]=100*(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oclean']=='agree')+np.sum(b['claimquestionanswergpt4oclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oclean']=='disagree'))\n",
    "    a.at[0,\"GPT4otemp12 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp12clean']=='disagree'))\n",
    "    a.at[0,\"GPT4otemp07 Agree\"]=100*(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree'))/(np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='agree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4otemp07clean']=='disagree'))\n",
    "    a.at[0,\"GPT3.5 Agree\"]=100*(np.sum(b['claimquestionanswergpt35clean']=='strongly agree')+np.sum(b['claimquestionanswergpt35clean']=='agree'))/(np.sum(b['claimquestionanswergpt35clean']=='strongly agree')+np.sum(b['claimquestionanswergpt35clean']=='agree')+np.sum(b['claimquestionanswergpt35clean']=='strongly disagree')+np.sum(b['claimquestionanswergpt35clean']=='disagree'))\n",
    "    a.at[0,\"GPT4oProf Agree\"]=100*(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree'))/(np.sum(b['claimquestionanswergpt4oprofclean']=='strongly agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='agree')+np.sum(b['claimquestionanswergpt4oprofclean']=='strongly disagree')+np.sum(b['claimquestionanswergpt4oprofclean']=='disagree'))\n",
    "\n",
    "    a.at[0,\"GPT4o Median\"]=b['claimquestionanswergpt4oclean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4otemp12 Median\"]=b['claimquestionanswergpt4otemp12clean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4otemp07 Median\"]=b['claimquestionanswergpt4otemp07clean'].value_counts().idxmax()    \n",
    "    a.at[0,\"GPT3.5 Median\"]=b['claimquestionanswergpt35clean'].value_counts().idxmax()\n",
    "    a.at[0,\"GPT4oProf Median\"]=b['claimquestionanswergpt4oprofclean'].value_counts().idxmax()\n",
    "\n",
    "    a.at[0,\"GPT4o MaxValue\"]=b['claimquestionanswergpt4oclean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4otemp12 MaxValue\"]=b['claimquestionanswergpt4otemp12clean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4otemp07 MaxValue\"]=b['claimquestionanswergpt4otemp07clean'].value_counts().max()/(n/100) \n",
    "    a.at[0,\"GPT3.5 MaxValue\"]=b['claimquestionanswergpt35clean'].value_counts().max()/(n/100)\n",
    "    a.at[0,\"GPT4oProf MaxValue\"]=b['claimquestionanswergpt4oprofclean'].value_counts().max()/(n/100)\n",
    "\n",
    "    \n",
    "    allclaimssummaryGPT=pd.concat([allclaimssummaryGPT,a]).reset_index(drop=True)\n",
    "\n",
    "# now we put both GPT answers and Economic Profs answers in one dataframe\n",
    "allstats=pd.concat([allclaimssummary,allclaimssummaryGPT], axis=1)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "49145869-261e-42b6-8a9a-3fd6a138b81a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we put both OLD and Current GPT answers  in one dataframe\n",
    "alltogether=pd.concat([allclaimssummaryGPT,allclaimssummaryGPTold], axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1deae007-b2e0-438e-9be0-163e40cfe758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.985539</td>\n",
       "      <td>0.226669</td>\n",
       "      <td>0.236853</td>\n",
       "      <td>0.923615</td>\n",
       "      <td>0.901775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <td>0.985539</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.224047</td>\n",
       "      <td>0.233689</td>\n",
       "      <td>0.924026</td>\n",
       "      <td>0.905426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <td>0.226669</td>\n",
       "      <td>0.224047</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.973312</td>\n",
       "      <td>0.032093</td>\n",
       "      <td>-0.014710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <td>0.236853</td>\n",
       "      <td>0.233689</td>\n",
       "      <td>0.973312</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052230</td>\n",
       "      <td>0.005813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "      <td>0.923615</td>\n",
       "      <td>0.924026</td>\n",
       "      <td>0.032093</td>\n",
       "      <td>0.052230</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.988287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "      <td>0.901775</td>\n",
       "      <td>0.905426</td>\n",
       "      <td>-0.014710</td>\n",
       "      <td>0.005813</td>\n",
       "      <td>0.988287</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GPT4o Opinion  GPT4o Opinion  GPT3.5 Opinion  \\\n",
       "GPT4o Opinion           1.000000       0.985539        0.226669   \n",
       "GPT4o Opinion           0.985539       1.000000        0.224047   \n",
       "GPT3.5 Opinion          0.226669       0.224047        1.000000   \n",
       "GPT3.5 Opinion          0.236853       0.233689        0.973312   \n",
       "GPT4oProf Opinion       0.923615       0.924026        0.032093   \n",
       "GPT4oProf Opinion       0.901775       0.905426       -0.014710   \n",
       "\n",
       "                   GPT3.5 Opinion  GPT4oProf Opinion  GPT4oProf Opinion  \n",
       "GPT4o Opinion            0.236853           0.923615           0.901775  \n",
       "GPT4o Opinion            0.233689           0.924026           0.905426  \n",
       "GPT3.5 Opinion           0.973312           0.032093          -0.014710  \n",
       "GPT3.5 Opinion           1.000000           0.052230           0.005813  \n",
       "GPT4oProf Opinion        0.052230           1.000000           0.988287  \n",
       "GPT4oProf Opinion        0.005813           0.988287           1.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# focus on correlations within the same ChatGPT version, shows almost perfect correlation over time\n",
    "# in terms of share that expresses an opinion\n",
    "# this suggest that sampling 200 was enough to get consistent results over time\n",
    "alltogether[['GPT4o Opinion',\t'GPT3.5 Opinion', 'GPT4oProf Opinion']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3e2c35e8-8d6d-45cc-910b-b704c8b02e77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978202</td>\n",
       "      <td>0.402139</td>\n",
       "      <td>0.381119</td>\n",
       "      <td>0.837599</td>\n",
       "      <td>0.871773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <td>0.978202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.445154</td>\n",
       "      <td>0.419526</td>\n",
       "      <td>0.771927</td>\n",
       "      <td>0.790432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <td>0.402139</td>\n",
       "      <td>0.445154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993695</td>\n",
       "      <td>0.452019</td>\n",
       "      <td>0.465379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <td>0.381119</td>\n",
       "      <td>0.419526</td>\n",
       "      <td>0.993695</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.443416</td>\n",
       "      <td>0.455905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "      <td>0.837599</td>\n",
       "      <td>0.771927</td>\n",
       "      <td>0.452019</td>\n",
       "      <td>0.443416</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.992855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "      <td>0.871773</td>\n",
       "      <td>0.790432</td>\n",
       "      <td>0.465379</td>\n",
       "      <td>0.455905</td>\n",
       "      <td>0.992855</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 GPT4o Agree  GPT4o Agree  GPT3.5 Agree  GPT3.5 Agree  \\\n",
       "GPT4o Agree         1.000000     0.978202      0.402139      0.381119   \n",
       "GPT4o Agree         0.978202     1.000000      0.445154      0.419526   \n",
       "GPT3.5 Agree        0.402139     0.445154      1.000000      0.993695   \n",
       "GPT3.5 Agree        0.381119     0.419526      0.993695      1.000000   \n",
       "GPT4oProf Agree     0.837599     0.771927      0.452019      0.443416   \n",
       "GPT4oProf Agree     0.871773     0.790432      0.465379      0.455905   \n",
       "\n",
       "                 GPT4oProf Agree  GPT4oProf Agree  \n",
       "GPT4o Agree             0.837599         0.871773  \n",
       "GPT4o Agree             0.771927         0.790432  \n",
       "GPT3.5 Agree            0.452019         0.465379  \n",
       "GPT3.5 Agree            0.443416         0.455905  \n",
       "GPT4oProf Agree         1.000000         0.992855  \n",
       "GPT4oProf Agree         0.992855         1.000000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# strong correlation in share that agrees over time\n",
    "alltogether[['GPT4o Agree',\t'GPT3.5 Agree', 'GPT4oProf Agree']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "df146806-6d2e-4a53-8218-41673d0cca7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPT4o Median</th>\n",
       "      <th>GPT4otemp12 Median</th>\n",
       "      <th>GPT4otemp07 Median</th>\n",
       "      <th>GPT3.5 Median</th>\n",
       "      <th>GPT4oProf Median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>disagree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "      <td>disagree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>strongly agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>strongly agree</td>\n",
       "      <td>uncertain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>uncertain</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>agree</td>\n",
       "      <td>strongly disagree</td>\n",
       "      <td>agree</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GPT4o Median GPT4otemp12 Median GPT4otemp07 Median      GPT3.5 Median  \\\n",
       "0            agree              agree              agree     strongly agree   \n",
       "1            agree              agree              agree     strongly agree   \n",
       "2        uncertain          uncertain          uncertain     strongly agree   \n",
       "3            agree              agree              agree              agree   \n",
       "4            agree              agree              agree     strongly agree   \n",
       "5            agree              agree              agree     strongly agree   \n",
       "6        uncertain          uncertain          uncertain              agree   \n",
       "7            agree              agree              agree              agree   \n",
       "8            agree              agree              agree              agree   \n",
       "9            agree              agree              agree              agree   \n",
       "10  strongly agree     strongly agree     strongly agree     strongly agree   \n",
       "11       uncertain          uncertain          uncertain              agree   \n",
       "12           agree              agree              agree              agree   \n",
       "13           agree              agree              agree              agree   \n",
       "14           agree              agree              agree  strongly disagree   \n",
       "15       uncertain          uncertain          uncertain              agree   \n",
       "16           agree              agree              agree              agree   \n",
       "17           agree              agree              agree  strongly disagree   \n",
       "18           agree              agree              agree              agree   \n",
       "19       uncertain          uncertain          uncertain  strongly disagree   \n",
       "20       uncertain          uncertain          uncertain              agree   \n",
       "21       uncertain          uncertain          uncertain              agree   \n",
       "22        disagree           disagree           disagree  strongly disagree   \n",
       "23           agree              agree              agree  strongly disagree   \n",
       "24           agree              agree              agree     strongly agree   \n",
       "25       uncertain          uncertain          uncertain           disagree   \n",
       "26           agree              agree              agree     strongly agree   \n",
       "27       uncertain          uncertain          uncertain              agree   \n",
       "28           agree              agree              agree              agree   \n",
       "29           agree              agree              agree              agree   \n",
       "30           agree              agree              agree              agree   \n",
       "31       uncertain          uncertain          uncertain              agree   \n",
       "32           agree              agree              agree              agree   \n",
       "33  strongly agree     strongly agree     strongly agree              agree   \n",
       "34           agree              agree              agree              agree   \n",
       "35           agree              agree              agree              agree   \n",
       "36           agree              agree              agree              agree   \n",
       "37       uncertain          uncertain          uncertain              agree   \n",
       "38       uncertain          uncertain          uncertain  strongly disagree   \n",
       "39       uncertain          uncertain          uncertain     strongly agree   \n",
       "40           agree              agree              agree  strongly disagree   \n",
       "41           agree              agree              agree     strongly agree   \n",
       "42           agree              agree              agree     strongly agree   \n",
       "43           agree              agree              agree              agree   \n",
       "44        disagree           disagree           disagree           disagree   \n",
       "45           agree              agree              agree     strongly agree   \n",
       "46       uncertain          uncertain          uncertain  strongly disagree   \n",
       "47       uncertain          uncertain          uncertain              agree   \n",
       "48           agree              agree              agree  strongly disagree   \n",
       "49           agree              agree              agree              agree   \n",
       "50           agree              agree              agree     strongly agree   \n",
       "51       uncertain          uncertain          uncertain  strongly disagree   \n",
       "52  strongly agree     strongly agree     strongly agree     strongly agree   \n",
       "53           agree              agree              agree              agree   \n",
       "54           agree              agree              agree              agree   \n",
       "55       uncertain          uncertain          uncertain  strongly disagree   \n",
       "56       uncertain          uncertain          uncertain     strongly agree   \n",
       "57       uncertain          uncertain          uncertain     strongly agree   \n",
       "58       uncertain          uncertain          uncertain          uncertain   \n",
       "59           agree              agree              agree  strongly disagree   \n",
       "\n",
       "   GPT4oProf Median  \n",
       "0             agree  \n",
       "1             agree  \n",
       "2         uncertain  \n",
       "3             agree  \n",
       "4    strongly agree  \n",
       "5             agree  \n",
       "6             agree  \n",
       "7             agree  \n",
       "8             agree  \n",
       "9             agree  \n",
       "10   strongly agree  \n",
       "11        uncertain  \n",
       "12            agree  \n",
       "13            agree  \n",
       "14            agree  \n",
       "15        uncertain  \n",
       "16            agree  \n",
       "17            agree  \n",
       "18            agree  \n",
       "19        uncertain  \n",
       "20        uncertain  \n",
       "21        uncertain  \n",
       "22         disagree  \n",
       "23   strongly agree  \n",
       "24            agree  \n",
       "25            agree  \n",
       "26            agree  \n",
       "27        uncertain  \n",
       "28            agree  \n",
       "29            agree  \n",
       "30            agree  \n",
       "31        uncertain  \n",
       "32   strongly agree  \n",
       "33   strongly agree  \n",
       "34            agree  \n",
       "35            agree  \n",
       "36            agree  \n",
       "37        uncertain  \n",
       "38        uncertain  \n",
       "39        uncertain  \n",
       "40            agree  \n",
       "41            agree  \n",
       "42            agree  \n",
       "43            agree  \n",
       "44         disagree  \n",
       "45   strongly agree  \n",
       "46        uncertain  \n",
       "47        uncertain  \n",
       "48            agree  \n",
       "49            agree  \n",
       "50            agree  \n",
       "51        uncertain  \n",
       "52   strongly agree  \n",
       "53            agree  \n",
       "54            agree  \n",
       "55        uncertain  \n",
       "56        uncertain  \n",
       "57        uncertain  \n",
       "58            agree  \n",
       "59            agree  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now we focus on most recent results and analyse temperature\n",
    "# is most frequently occuring answer the same\n",
    "allclaimssummaryGPT[['GPT4o Median','GPT4otemp12 Median','GPT4otemp07 Median','GPT3.5 Median', 'GPT4oProf Median']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f32e6cf4-7fab-4a82-93f6-d5f5025e92a5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60\n",
      "60\n",
      "23\n",
      "53\n"
     ]
    }
   ],
   "source": [
    "# most frequent is the same for all temps, default 4o is similar to ProfPersona4o but 3.5 is very different\n",
    "print(np.sum(allclaimssummaryGPT['GPT4o Median']==allclaimssummaryGPT['GPT4otemp12 Median']))\n",
    "print(np.sum(allclaimssummaryGPT['GPT4o Median']==allclaimssummaryGPT['GPT4otemp07 Median']))\n",
    "print(np.sum(allclaimssummaryGPT['GPT4o Median']==allclaimssummaryGPT['GPT3.5 Median']))\n",
    "print(np.sum(allclaimssummaryGPT['GPT4o Median']==allclaimssummaryGPT['GPT4oProf Median']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a2c6114-4168-44f3-a1d8-be277863178c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <th>GPT4otemp12 Opinion</th>\n",
       "      <th>GPT4otemp07 Opinion</th>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GPT4o Opinion</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.997768</td>\n",
       "      <td>0.997144</td>\n",
       "      <td>0.226669</td>\n",
       "      <td>0.923615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4otemp12 Opinion</th>\n",
       "      <td>0.997768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.993291</td>\n",
       "      <td>0.209343</td>\n",
       "      <td>0.928627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4otemp07 Opinion</th>\n",
       "      <td>0.997144</td>\n",
       "      <td>0.993291</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.233286</td>\n",
       "      <td>0.921250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Opinion</th>\n",
       "      <td>0.226669</td>\n",
       "      <td>0.209343</td>\n",
       "      <td>0.233286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Opinion</th>\n",
       "      <td>0.923615</td>\n",
       "      <td>0.928627</td>\n",
       "      <td>0.921250</td>\n",
       "      <td>0.032093</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     GPT4o Opinion  GPT4otemp12 Opinion  GPT4otemp07 Opinion  \\\n",
       "GPT4o Opinion             1.000000             0.997768             0.997144   \n",
       "GPT4otemp12 Opinion       0.997768             1.000000             0.993291   \n",
       "GPT4otemp07 Opinion       0.997144             0.993291             1.000000   \n",
       "GPT3.5 Opinion            0.226669             0.209343             0.233286   \n",
       "GPT4oProf Opinion         0.923615             0.928627             0.921250   \n",
       "\n",
       "                     GPT3.5 Opinion  GPT4oProf Opinion  \n",
       "GPT4o Opinion              0.226669           0.923615  \n",
       "GPT4otemp12 Opinion        0.209343           0.928627  \n",
       "GPT4otemp07 Opinion        0.233286           0.921250  \n",
       "GPT3.5 Opinion             1.000000           0.032093  \n",
       "GPT4oProf Opinion          0.032093           1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# strong correlation accross temps and persona but 3.5 stands out, for share having an opinion\n",
    "allclaimssummaryGPT[['GPT4o Opinion','GPT4otemp12 Opinion','GPT4otemp07 Opinion',\t'GPT3.5 Opinion', 'GPT4oProf Opinion']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d03af657-f60a-4e6f-8fbb-70c12f5fd69e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <th>GPT4otemp12 Agree</th>\n",
       "      <th>GPT4otemp07 Agree</th>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GPT4o Agree</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978132</td>\n",
       "      <td>0.924312</td>\n",
       "      <td>0.402139</td>\n",
       "      <td>0.837599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4otemp12 Agree</th>\n",
       "      <td>0.978132</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.898446</td>\n",
       "      <td>0.485639</td>\n",
       "      <td>0.858834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4otemp07 Agree</th>\n",
       "      <td>0.924312</td>\n",
       "      <td>0.898446</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.319956</td>\n",
       "      <td>0.867712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT3.5 Agree</th>\n",
       "      <td>0.402139</td>\n",
       "      <td>0.485639</td>\n",
       "      <td>0.319956</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.452019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GPT4oProf Agree</th>\n",
       "      <td>0.837599</td>\n",
       "      <td>0.858834</td>\n",
       "      <td>0.867712</td>\n",
       "      <td>0.452019</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   GPT4o Agree  GPT4otemp12 Agree  GPT4otemp07 Agree  \\\n",
       "GPT4o Agree           1.000000           0.978132           0.924312   \n",
       "GPT4otemp12 Agree     0.978132           1.000000           0.898446   \n",
       "GPT4otemp07 Agree     0.924312           0.898446           1.000000   \n",
       "GPT3.5 Agree          0.402139           0.485639           0.319956   \n",
       "GPT4oProf Agree       0.837599           0.858834           0.867712   \n",
       "\n",
       "                   GPT3.5 Agree  GPT4oProf Agree  \n",
       "GPT4o Agree            0.402139         0.837599  \n",
       "GPT4otemp12 Agree      0.485639         0.858834  \n",
       "GPT4otemp07 Agree      0.319956         0.867712  \n",
       "GPT3.5 Agree           1.000000         0.452019  \n",
       "GPT4oProf Agree        0.452019         1.000000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# strong correlation in share agree accross temps and persona but 3.5 stands out\n",
    "allclaimssummaryGPT[['GPT4o Agree','GPT4otemp12 Agree','GPT4otemp07 Agree',\t'GPT3.5 Agree', 'GPT4oProf Agree']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a9be8488-f3fb-4882-93c9-da5059438198",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Euro/US</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Strongly Agree</th>\n",
       "      <th>Agree</th>\n",
       "      <th>Disagree</th>\n",
       "      <th>Strongly Disagree</th>\n",
       "      <th>Uncertain</th>\n",
       "      <th>No Opinion</th>\n",
       "      <th>#Answered</th>\n",
       "      <th>Have An Opinion</th>\n",
       "      <th>Agree/Have An Opinion</th>\n",
       "      <th>Median</th>\n",
       "      <th>MaxValue</th>\n",
       "      <th>Chance Top Prof Choice</th>\n",
       "      <th>Chance One Off Prof Choice</th>\n",
       "      <th>Chance More than One Off Prof Choice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Together</td>\n",
       "      <td>A ban on advertising junk foods (those that ar...</td>\n",
       "      <td>3.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>82</td>\n",
       "      <td>61.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>42.7</td>\n",
       "      <td>7.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Together</td>\n",
       "      <td>A federal minimum wage that is pegged to state...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>78</td>\n",
       "      <td>65.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>Agree</td>\n",
       "      <td>43.6</td>\n",
       "      <td>43.6</td>\n",
       "      <td>43.7</td>\n",
       "      <td>1.270000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Together</td>\n",
       "      <td>A global corporate tax system that is based on...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75</td>\n",
       "      <td>54.7</td>\n",
       "      <td>97.6</td>\n",
       "      <td>Agree</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>54.6</td>\n",
       "      <td>1.400000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Together</td>\n",
       "      <td>A global minimum corporate tax rate would limi...</td>\n",
       "      <td>22.7</td>\n",
       "      <td>66.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75</td>\n",
       "      <td>90.7</td>\n",
       "      <td>98.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>66.7</td>\n",
       "      <td>66.7</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Together</td>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>63.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>84.4</td>\n",
       "      <td>96.9</td>\n",
       "      <td>Agree</td>\n",
       "      <td>63.6</td>\n",
       "      <td>63.6</td>\n",
       "      <td>33.8</td>\n",
       "      <td>2.600000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Together</td>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>49.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>55.8</td>\n",
       "      <td>93.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>49.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>46.8</td>\n",
       "      <td>3.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Together</td>\n",
       "      <td>A price cap imposed by the G7/EU countries on ...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>51.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>28.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>77</td>\n",
       "      <td>67.5</td>\n",
       "      <td>86.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.9</td>\n",
       "      <td>39.0</td>\n",
       "      <td>9.100000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Together</td>\n",
       "      <td>As of now, there needs to be more government r...</td>\n",
       "      <td>14.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>81</td>\n",
       "      <td>71.6</td>\n",
       "      <td>79.3</td>\n",
       "      <td>Agree</td>\n",
       "      <td>42.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>43.2</td>\n",
       "      <td>1.480000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Together</td>\n",
       "      <td>Central banks that do not introduce their own ...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>22.2</td>\n",
       "      <td>31.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>27.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>72</td>\n",
       "      <td>58.3</td>\n",
       "      <td>40.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>31.9</td>\n",
       "      <td>31.9</td>\n",
       "      <td>44.5</td>\n",
       "      <td>2.360000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Together</td>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>26.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>82</td>\n",
       "      <td>41.5</td>\n",
       "      <td>70.6</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>53.7</td>\n",
       "      <td>53.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>7.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Together</td>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>4.9</td>\n",
       "      <td>57.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>82</td>\n",
       "      <td>62.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>57.3</td>\n",
       "      <td>57.3</td>\n",
       "      <td>42.7</td>\n",
       "      <td>7.105427e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Together</td>\n",
       "      <td>Having the opportunity to work two to three da...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>82</td>\n",
       "      <td>42.7</td>\n",
       "      <td>62.9</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>54.9</td>\n",
       "      <td>54.9</td>\n",
       "      <td>40.2</td>\n",
       "      <td>4.900000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Together</td>\n",
       "      <td>High tariffs imposed by the European Union on ...</td>\n",
       "      <td>11.7</td>\n",
       "      <td>54.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>72.7</td>\n",
       "      <td>91.1</td>\n",
       "      <td>Agree</td>\n",
       "      <td>54.5</td>\n",
       "      <td>54.5</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.500000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Together</td>\n",
       "      <td>In pursuit of credible research designs, resea...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>42.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80</td>\n",
       "      <td>61.2</td>\n",
       "      <td>83.7</td>\n",
       "      <td>Agree</td>\n",
       "      <td>42.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>47.6</td>\n",
       "      <td>9.900000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Together</td>\n",
       "      <td>Network externalities give Twitter an incumben...</td>\n",
       "      <td>29.6</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>81</td>\n",
       "      <td>92.6</td>\n",
       "      <td>93.3</td>\n",
       "      <td>Agree</td>\n",
       "      <td>56.8</td>\n",
       "      <td>56.8</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Together</td>\n",
       "      <td>Policies that aim to reduce obesity by increas...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>51.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>82</td>\n",
       "      <td>42.7</td>\n",
       "      <td>57.1</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>51.2</td>\n",
       "      <td>51.2</td>\n",
       "      <td>37.9</td>\n",
       "      <td>1.090000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Together</td>\n",
       "      <td>Stablecoins that are not fully backed by eithe...</td>\n",
       "      <td>38.7</td>\n",
       "      <td>50.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>75</td>\n",
       "      <td>90.7</td>\n",
       "      <td>98.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>50.7</td>\n",
       "      <td>50.7</td>\n",
       "      <td>48.1</td>\n",
       "      <td>1.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Together</td>\n",
       "      <td>Targeting the Russian economy through a total ...</td>\n",
       "      <td>11.1</td>\n",
       "      <td>49.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>81</td>\n",
       "      <td>63.0</td>\n",
       "      <td>96.1</td>\n",
       "      <td>Agree</td>\n",
       "      <td>49.4</td>\n",
       "      <td>49.4</td>\n",
       "      <td>48.2</td>\n",
       "      <td>2.400000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Together</td>\n",
       "      <td>The Bank for International Settlements defines...</td>\n",
       "      <td>9.7</td>\n",
       "      <td>40.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>72</td>\n",
       "      <td>52.8</td>\n",
       "      <td>94.7</td>\n",
       "      <td>Agree</td>\n",
       "      <td>40.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>56.9</td>\n",
       "      <td>2.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Together</td>\n",
       "      <td>The UK economy is likely to be at least severa...</td>\n",
       "      <td>23.8</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>84</td>\n",
       "      <td>82.1</td>\n",
       "      <td>98.6</td>\n",
       "      <td>Agree</td>\n",
       "      <td>57.1</td>\n",
       "      <td>57.1</td>\n",
       "      <td>41.7</td>\n",
       "      <td>1.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Together</td>\n",
       "      <td>The aggregate economy of the 27 countries stil...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>2.4</td>\n",
       "      <td>84</td>\n",
       "      <td>56.0</td>\n",
       "      <td>36.2</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>41.7</td>\n",
       "      <td>41.7</td>\n",
       "      <td>47.6</td>\n",
       "      <td>1.070000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Together</td>\n",
       "      <td>The current US federal minimum wage is $7.25 p...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>78</td>\n",
       "      <td>55.1</td>\n",
       "      <td>69.8</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>42.3</td>\n",
       "      <td>42.3</td>\n",
       "      <td>52.6</td>\n",
       "      <td>5.100000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Together</td>\n",
       "      <td>The economic and financial sanctions already i...</td>\n",
       "      <td>24.7</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>81</td>\n",
       "      <td>87.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>63.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Together</td>\n",
       "      <td>The introduction of a central bank digital cur...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>72</td>\n",
       "      <td>62.5</td>\n",
       "      <td>82.2</td>\n",
       "      <td>Agree</td>\n",
       "      <td>50.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>38.9</td>\n",
       "      <td>1.110000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Together</td>\n",
       "      <td>The introduction of natural experiments to eco...</td>\n",
       "      <td>63.8</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>80</td>\n",
       "      <td>97.5</td>\n",
       "      <td>98.7</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>63.8</td>\n",
       "      <td>63.8</td>\n",
       "      <td>32.5</td>\n",
       "      <td>3.700000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Together</td>\n",
       "      <td>The oil price cap imposed by the G7/EU countri...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>40.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>77</td>\n",
       "      <td>54.5</td>\n",
       "      <td>83.3</td>\n",
       "      <td>Agree</td>\n",
       "      <td>40.3</td>\n",
       "      <td>40.3</td>\n",
       "      <td>50.7</td>\n",
       "      <td>9.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Together</td>\n",
       "      <td>The ‘credibility revolution’ in empirical econ...</td>\n",
       "      <td>47.5</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>93.8</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>47.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>46.2</td>\n",
       "      <td>6.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Together</td>\n",
       "      <td>Artificial intelligence is likely to be a high...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>52.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>65</td>\n",
       "      <td>63.1</td>\n",
       "      <td>87.8</td>\n",
       "      <td>Agree</td>\n",
       "      <td>52.3</td>\n",
       "      <td>52.3</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7.700000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Together</td>\n",
       "      <td>Artificial intelligence offers substantial opp...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>35.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>50.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>65</td>\n",
       "      <td>46.2</td>\n",
       "      <td>83.3</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>50.8</td>\n",
       "      <td>50.8</td>\n",
       "      <td>41.6</td>\n",
       "      <td>7.600000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Together</td>\n",
       "      <td>By enabling women’s life choices about educati...</td>\n",
       "      <td>37.7</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>98.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>61.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Together</td>\n",
       "      <td>Even if Argentina could marshal the resources ...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>36.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>68</td>\n",
       "      <td>50.0</td>\n",
       "      <td>79.4</td>\n",
       "      <td>Agree</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.8</td>\n",
       "      <td>52.9</td>\n",
       "      <td>1.030000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Together</td>\n",
       "      <td>Financial regulators in the US and Europe lack...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>72.6</td>\n",
       "      <td>50.9</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>31.5</td>\n",
       "      <td>3.700000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Together</td>\n",
       "      <td>Fiscal rules on budget deficits and public deb...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>69</td>\n",
       "      <td>71.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>40.6</td>\n",
       "      <td>40.6</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1.740000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Together</td>\n",
       "      <td>Fully guaranteeing uninsured deposits at Silic...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>43.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>74.0</td>\n",
       "      <td>77.8</td>\n",
       "      <td>Agree</td>\n",
       "      <td>43.8</td>\n",
       "      <td>43.8</td>\n",
       "      <td>39.7</td>\n",
       "      <td>1.650000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Together</td>\n",
       "      <td>Gender gaps in today’s labor market arise less...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>88.3</td>\n",
       "      <td>94.1</td>\n",
       "      <td>Agree</td>\n",
       "      <td>70.1</td>\n",
       "      <td>70.1</td>\n",
       "      <td>24.7</td>\n",
       "      <td>5.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Together</td>\n",
       "      <td>Given current regulations, non-bank financial ...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>70</td>\n",
       "      <td>52.9</td>\n",
       "      <td>73.0</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>38.6</td>\n",
       "      <td>38.6</td>\n",
       "      <td>45.7</td>\n",
       "      <td>1.570000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Together</td>\n",
       "      <td>In the absence of continuing flows of Western ...</td>\n",
       "      <td>58.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>80</td>\n",
       "      <td>96.2</td>\n",
       "      <td>98.7</td>\n",
       "      <td>Strongly Agree</td>\n",
       "      <td>58.8</td>\n",
       "      <td>58.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Together</td>\n",
       "      <td>Non-bank financial intermediaries pose a subst...</td>\n",
       "      <td>11.4</td>\n",
       "      <td>65.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>70</td>\n",
       "      <td>82.9</td>\n",
       "      <td>93.1</td>\n",
       "      <td>Agree</td>\n",
       "      <td>65.7</td>\n",
       "      <td>65.7</td>\n",
       "      <td>28.5</td>\n",
       "      <td>5.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Together</td>\n",
       "      <td>Not guaranteeing uninsured deposits at Silicon...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>50.7</td>\n",
       "      <td>70.3</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>45.2</td>\n",
       "      <td>45.2</td>\n",
       "      <td>45.2</td>\n",
       "      <td>9.600000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Together</td>\n",
       "      <td>Regulating the leverage and liquidity of non-b...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0</td>\n",
       "      <td>95.9</td>\n",
       "      <td>Agree</td>\n",
       "      <td>57.1</td>\n",
       "      <td>57.1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2.900000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Together</td>\n",
       "      <td>Responses To Market Power Constraints on the a...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>27.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>72</td>\n",
       "      <td>45.8</td>\n",
       "      <td>63.6</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>43.1</td>\n",
       "      <td>43.1</td>\n",
       "      <td>44.5</td>\n",
       "      <td>1.240000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Together</td>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>46.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>69</td>\n",
       "      <td>56.5</td>\n",
       "      <td>87.2</td>\n",
       "      <td>Agree</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.3</td>\n",
       "      <td>7.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Together</td>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>47.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>69</td>\n",
       "      <td>34.8</td>\n",
       "      <td>37.5</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>47.8</td>\n",
       "      <td>47.8</td>\n",
       "      <td>33.3</td>\n",
       "      <td>1.890000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Together</td>\n",
       "      <td>Subsidizing Green Technology Government subsid...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>72</td>\n",
       "      <td>87.5</td>\n",
       "      <td>96.8</td>\n",
       "      <td>Agree</td>\n",
       "      <td>59.7</td>\n",
       "      <td>59.7</td>\n",
       "      <td>37.5</td>\n",
       "      <td>2.800000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Together</td>\n",
       "      <td>The economic and financial sanctions against R...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>38.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>80</td>\n",
       "      <td>65.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>38.8</td>\n",
       "      <td>38.8</td>\n",
       "      <td>37.5</td>\n",
       "      <td>2.370000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Together</td>\n",
       "      <td>The effectiveness of existing antitrust regime...</td>\n",
       "      <td>9.7</td>\n",
       "      <td>54.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>72</td>\n",
       "      <td>68.1</td>\n",
       "      <td>93.9</td>\n",
       "      <td>Agree</td>\n",
       "      <td>54.2</td>\n",
       "      <td>54.2</td>\n",
       "      <td>41.7</td>\n",
       "      <td>4.100000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Together</td>\n",
       "      <td>The fundamental cause of Argentina’s high infl...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>68</td>\n",
       "      <td>89.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>64.7</td>\n",
       "      <td>64.7</td>\n",
       "      <td>35.3</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Together</td>\n",
       "      <td>The gender gap in pay would be substantially r...</td>\n",
       "      <td>11.7</td>\n",
       "      <td>59.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>72.7</td>\n",
       "      <td>98.2</td>\n",
       "      <td>Agree</td>\n",
       "      <td>59.7</td>\n",
       "      <td>59.7</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1.300000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Together</td>\n",
       "      <td>The proposed US tariffs on Chinese EVs would l...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>53.6</td>\n",
       "      <td>75.6</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>46.4</td>\n",
       "      <td>46.4</td>\n",
       "      <td>52.4</td>\n",
       "      <td>1.200000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Together</td>\n",
       "      <td>The proposed US tariffs on Chinese EVs would m...</td>\n",
       "      <td>10.7</td>\n",
       "      <td>59.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>76.2</td>\n",
       "      <td>92.2</td>\n",
       "      <td>Agree</td>\n",
       "      <td>59.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>6.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>65</td>\n",
       "      <td>58.5</td>\n",
       "      <td>52.6</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>32.3</td>\n",
       "      <td>32.3</td>\n",
       "      <td>55.4</td>\n",
       "      <td>1.230000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>12.3</td>\n",
       "      <td>56.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "      <td>75.4</td>\n",
       "      <td>91.8</td>\n",
       "      <td>Agree</td>\n",
       "      <td>56.9</td>\n",
       "      <td>56.9</td>\n",
       "      <td>37.0</td>\n",
       "      <td>6.100000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>33.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "      <td>73.8</td>\n",
       "      <td>47.9</td>\n",
       "      <td>Agree</td>\n",
       "      <td>33.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.850000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence is likely to le...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>35.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>65</td>\n",
       "      <td>50.8</td>\n",
       "      <td>84.8</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>44.6</td>\n",
       "      <td>44.6</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1.230000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>65</td>\n",
       "      <td>49.2</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>49.2</td>\n",
       "      <td>49.2</td>\n",
       "      <td>43.1</td>\n",
       "      <td>7.700000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>43.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>53.8</td>\n",
       "      <td>85.7</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.2</td>\n",
       "      <td>50.8</td>\n",
       "      <td>3.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>27.3</td>\n",
       "      <td>61.9</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>72.7</td>\n",
       "      <td>72.7</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>1.3</td>\n",
       "      <td>44.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>51.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>48.1</td>\n",
       "      <td>94.6</td>\n",
       "      <td>Uncertain</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.9</td>\n",
       "      <td>45.5</td>\n",
       "      <td>2.600000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>46.2</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>46.2</td>\n",
       "      <td>46.2</td>\n",
       "      <td>23.1</td>\n",
       "      <td>3.070000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Together</td>\n",
       "      <td>Using subsidies for green technologies instead...</td>\n",
       "      <td>18.1</td>\n",
       "      <td>37.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>72</td>\n",
       "      <td>70.8</td>\n",
       "      <td>78.4</td>\n",
       "      <td>Agree</td>\n",
       "      <td>37.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>47.3</td>\n",
       "      <td>1.520000e+01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Euro/US                                              Claim  \\\n",
       "0   Together  A ban on advertising junk foods (those that ar...   \n",
       "1   Together  A federal minimum wage that is pegged to state...   \n",
       "2   Together  A global corporate tax system that is based on...   \n",
       "3   Together  A global minimum corporate tax rate would limi...   \n",
       "4   Together  A mandate for public companies to provide clim...   \n",
       "5   Together  A mandate for public companies to provide clim...   \n",
       "6   Together  A price cap imposed by the G7/EU countries on ...   \n",
       "7   Together  As of now, there needs to be more government r...   \n",
       "8   Together  Central banks that do not introduce their own ...   \n",
       "9   Together  Employees who spend two of their days each wee...   \n",
       "10  Together  Employees who spend two of their days each wee...   \n",
       "11  Together  Having the opportunity to work two to three da...   \n",
       "12  Together  High tariffs imposed by the European Union on ...   \n",
       "13  Together  In pursuit of credible research designs, resea...   \n",
       "14  Together  Network externalities give Twitter an incumben...   \n",
       "15  Together  Policies that aim to reduce obesity by increas...   \n",
       "16  Together  Stablecoins that are not fully backed by eithe...   \n",
       "17  Together  Targeting the Russian economy through a total ...   \n",
       "18  Together  The Bank for International Settlements defines...   \n",
       "19  Together  The UK economy is likely to be at least severa...   \n",
       "20  Together  The aggregate economy of the 27 countries stil...   \n",
       "21  Together  The current US federal minimum wage is $7.25 p...   \n",
       "22  Together  The economic and financial sanctions already i...   \n",
       "23  Together  The introduction of a central bank digital cur...   \n",
       "24  Together  The introduction of natural experiments to eco...   \n",
       "25  Together  The oil price cap imposed by the G7/EU countri...   \n",
       "26  Together  The ‘credibility revolution’ in empirical econ...   \n",
       "27  Together  Artificial intelligence is likely to be a high...   \n",
       "28  Together  Artificial intelligence offers substantial opp...   \n",
       "29  Together  By enabling women’s life choices about educati...   \n",
       "30  Together  Even if Argentina could marshal the resources ...   \n",
       "31  Together  Financial regulators in the US and Europe lack...   \n",
       "32  Together  Fiscal rules on budget deficits and public deb...   \n",
       "33  Together  Fully guaranteeing uninsured deposits at Silic...   \n",
       "34  Together  Gender gaps in today’s labor market arise less...   \n",
       "35  Together  Given current regulations, non-bank financial ...   \n",
       "36  Together  In the absence of continuing flows of Western ...   \n",
       "37  Together  Non-bank financial intermediaries pose a subst...   \n",
       "38  Together  Not guaranteeing uninsured deposits at Silicon...   \n",
       "39  Together  Regulating the leverage and liquidity of non-b...   \n",
       "40  Together  Responses To Market Power Constraints on the a...   \n",
       "41  Together  Since the inception of the Stability and Growt...   \n",
       "42  Together  Since the inception of the Stability and Growt...   \n",
       "43  Together  Subsidizing Green Technology Government subsid...   \n",
       "44  Together  The economic and financial sanctions against R...   \n",
       "45  Together  The effectiveness of existing antitrust regime...   \n",
       "46  Together  The fundamental cause of Argentina’s high infl...   \n",
       "47  Together  The gender gap in pay would be substantially r...   \n",
       "48  Together  The proposed US tariffs on Chinese EVs would l...   \n",
       "49  Together  The proposed US tariffs on Chinese EVs would m...   \n",
       "50  Together  The response to recent bank failures should be...   \n",
       "51  Together  The response to recent bank failures should be...   \n",
       "52  Together  The response to recent bank failures should be...   \n",
       "53  Together  Use of artificial intelligence is likely to le...   \n",
       "54  Together  Use of artificial intelligence over the next t...   \n",
       "55  Together  Use of artificial intelligence over the next t...   \n",
       "56  Together  Use of artificial intelligence over the next t...   \n",
       "57  Together  Use of artificial intelligence over the next t...   \n",
       "58  Together  Use of artificial intelligence over the next t...   \n",
       "59  Together  Using subsidies for green technologies instead...   \n",
       "\n",
       "    Strongly Agree  Agree  Disagree  Strongly Disagree  Uncertain  No Opinion  \\\n",
       "0              3.7   50.0       7.3                0.0       32.9         6.1   \n",
       "1              9.0   43.6      12.8                0.0       32.1         2.6   \n",
       "2              9.3   44.0       1.3                0.0       41.3         4.0   \n",
       "3             22.7   66.7       1.3                0.0        5.3         4.0   \n",
       "4             18.2   63.6       2.6                0.0       14.3         1.3   \n",
       "5              2.6   49.4       2.6                1.3       42.9         1.3   \n",
       "6              6.5   51.9       7.8                1.3       28.6         3.9   \n",
       "7             14.8   42.0       9.9                4.9       27.2         1.2   \n",
       "8              1.4   22.2      31.9                2.8       27.8        13.9   \n",
       "9              2.4   26.8      12.2                0.0       53.7         4.9   \n",
       "10             4.9   57.3       0.0                0.0       32.9         4.9   \n",
       "11             1.2   25.6      14.6                1.2       54.9         2.4   \n",
       "12            11.7   54.5       6.5                0.0       26.0         1.3   \n",
       "13             8.8   42.5       8.8                1.2       33.8         5.0   \n",
       "14            29.6   56.8       6.2                0.0        4.9         2.5   \n",
       "15             2.4   22.0      15.9                2.4       51.2         6.1   \n",
       "16            38.7   50.7       1.3                0.0        2.7         6.7   \n",
       "17            11.1   49.4       2.5                0.0       34.6         2.5   \n",
       "18             9.7   40.3       2.8                0.0       33.3        13.9   \n",
       "19            23.8   57.1       1.2                0.0       15.5         2.4   \n",
       "20             4.8   15.5      32.1                3.6       41.7         2.4   \n",
       "21             2.6   35.9      16.7                0.0       42.3         2.6   \n",
       "22            24.7   63.0       0.0                0.0       11.1         1.2   \n",
       "23             1.4   50.0       9.7                1.4       25.0        12.5   \n",
       "24            63.8   32.5       1.2                0.0        1.2         1.2   \n",
       "25             5.2   40.3       7.8                1.3       39.0         6.5   \n",
       "26            47.5   46.2       0.0                0.0        6.2         0.0   \n",
       "27             3.1   52.3       6.2                1.5       35.4         1.5   \n",
       "28             3.1   35.4       6.2                1.5       50.8         3.1   \n",
       "29            37.7   61.0       0.0                0.0        1.3         0.0   \n",
       "30             2.9   36.8      10.3                0.0       33.8        16.2   \n",
       "31             6.8   30.1      31.5                4.1       23.3         4.1   \n",
       "32            13.0   40.6      17.4                0.0       23.2         5.8   \n",
       "33            13.7   43.8      15.1                1.4       21.9         4.1   \n",
       "34            13.0   70.1       3.9                1.3       11.7         0.0   \n",
       "35             7.1   31.4      14.3                0.0       38.6         8.6   \n",
       "36            58.8   36.2       0.0                1.2        1.2         2.5   \n",
       "37            11.4   65.7       5.7                0.0       10.0         7.1   \n",
       "38             5.5   30.1      15.1                0.0       45.2         4.1   \n",
       "39            10.0   57.1       1.4                1.4       22.9         7.1   \n",
       "40             1.4   27.8      16.7                0.0       43.1        11.1   \n",
       "41             2.9   46.4       7.2                0.0       27.5        15.9   \n",
       "42             0.0   13.0      20.3                1.4       47.8        17.4   \n",
       "43            25.0   59.7       2.8                0.0       11.1         1.4   \n",
       "44             2.5   38.8      23.8                0.0       28.8         6.2   \n",
       "45             9.7   54.2       4.2                0.0       29.2         2.8   \n",
       "46            25.0   64.7       0.0                0.0        1.5         8.8   \n",
       "47            11.7   59.7       1.3                0.0       27.3         0.0   \n",
       "48             0.0   40.5      11.9                1.2       46.4         0.0   \n",
       "49            10.7   59.5       4.8                1.2       23.8         0.0   \n",
       "50             0.0   30.8      24.6                3.1       32.3         9.2   \n",
       "51            12.3   56.9       4.6                1.5       18.5         6.2   \n",
       "52             1.5   33.8      33.8                4.6       20.0         6.2   \n",
       "53             7.7   35.4       7.7                0.0       44.6         4.6   \n",
       "54             6.2   30.8      12.3                0.0       49.2         1.5   \n",
       "55             3.1   43.1       7.7                0.0       46.2         0.0   \n",
       "56             0.0   16.9      10.4                0.0       72.7         0.0   \n",
       "57             1.3   44.2       1.3                1.3       51.9         0.0   \n",
       "58             3.1   46.2      27.7                3.1       20.0         0.0   \n",
       "59            18.1   37.5      15.3                0.0       26.4         2.8   \n",
       "\n",
       "   #Answered  Have An Opinion  Agree/Have An Opinion          Median  \\\n",
       "0         82             61.0                   88.0           Agree   \n",
       "1         78             65.4                   80.4           Agree   \n",
       "2         75             54.7                   97.6           Agree   \n",
       "3         75             90.7                   98.5           Agree   \n",
       "4         77             84.4                   96.9           Agree   \n",
       "5         77             55.8                   93.0           Agree   \n",
       "6         77             67.5                   86.5           Agree   \n",
       "7         81             71.6                   79.3           Agree   \n",
       "8         72             58.3                   40.5        Disagree   \n",
       "9         82             41.5                   70.6       Uncertain   \n",
       "10        82             62.2                  100.0           Agree   \n",
       "11        82             42.7                   62.9       Uncertain   \n",
       "12        77             72.7                   91.1           Agree   \n",
       "13        80             61.2                   83.7           Agree   \n",
       "14        81             92.6                   93.3           Agree   \n",
       "15        82             42.7                   57.1       Uncertain   \n",
       "16        75             90.7                   98.5           Agree   \n",
       "17        81             63.0                   96.1           Agree   \n",
       "18        72             52.8                   94.7           Agree   \n",
       "19        84             82.1                   98.6           Agree   \n",
       "20        84             56.0                   36.2       Uncertain   \n",
       "21        78             55.1                   69.8       Uncertain   \n",
       "22        81             87.7                  100.0           Agree   \n",
       "23        72             62.5                   82.2           Agree   \n",
       "24        80             97.5                   98.7  Strongly Agree   \n",
       "25        77             54.5                   83.3           Agree   \n",
       "26        80             93.8                  100.0  Strongly Agree   \n",
       "27        65             63.1                   87.8           Agree   \n",
       "28        65             46.2                   83.3       Uncertain   \n",
       "29        77             98.7                  100.0           Agree   \n",
       "30        68             50.0                   79.4           Agree   \n",
       "31        73             72.6                   50.9        Disagree   \n",
       "32        69             71.0                   75.5           Agree   \n",
       "33        73             74.0                   77.8           Agree   \n",
       "34        77             88.3                   94.1           Agree   \n",
       "35        70             52.9                   73.0       Uncertain   \n",
       "36        80             96.2                   98.7  Strongly Agree   \n",
       "37        70             82.9                   93.1           Agree   \n",
       "38        73             50.7                   70.3       Uncertain   \n",
       "39        70             70.0                   95.9           Agree   \n",
       "40        72             45.8                   63.6       Uncertain   \n",
       "41        69             56.5                   87.2           Agree   \n",
       "42        69             34.8                   37.5       Uncertain   \n",
       "43        72             87.5                   96.8           Agree   \n",
       "44        80             65.0                   63.5           Agree   \n",
       "45        72             68.1                   93.9           Agree   \n",
       "46        68             89.7                  100.0           Agree   \n",
       "47        77             72.7                   98.2           Agree   \n",
       "48        84             53.6                   75.6       Uncertain   \n",
       "49        84             76.2                   92.2           Agree   \n",
       "50        65             58.5                   52.6       Uncertain   \n",
       "51        65             75.4                   91.8           Agree   \n",
       "52        65             73.8                   47.9           Agree   \n",
       "53        65             50.8                   84.8       Uncertain   \n",
       "54        65             49.2                   75.0       Uncertain   \n",
       "55        65             53.8                   85.7       Uncertain   \n",
       "56        77             27.3                   61.9       Uncertain   \n",
       "57        77             48.1                   94.6       Uncertain   \n",
       "58        65             80.0                   61.5           Agree   \n",
       "59        72             70.8                   78.4           Agree   \n",
       "\n",
       "    MaxValue  Chance Top Prof Choice  Chance One Off Prof Choice  \\\n",
       "0       50.0                    50.0                        42.7   \n",
       "1       43.6                    43.6                        43.7   \n",
       "2       44.0                    44.0                        54.6   \n",
       "3       66.7                    66.7                        32.0   \n",
       "4       63.6                    63.6                        33.8   \n",
       "5       49.4                    49.4                        46.8   \n",
       "6       51.9                    51.9                        39.0   \n",
       "7       42.0                    42.0                        43.2   \n",
       "8       31.9                    31.9                        44.5   \n",
       "9       53.7                    53.7                        39.0   \n",
       "10      57.3                    57.3                        42.7   \n",
       "11      54.9                    54.9                        40.2   \n",
       "12      54.5                    54.5                        39.0   \n",
       "13      42.5                    42.5                        47.6   \n",
       "14      56.8                    56.8                        37.0   \n",
       "15      51.2                    51.2                        37.9   \n",
       "16      50.7                    50.7                        48.1   \n",
       "17      49.4                    49.4                        48.2   \n",
       "18      40.3                    40.3                        56.9   \n",
       "19      57.1                    57.1                        41.7   \n",
       "20      41.7                    41.7                        47.6   \n",
       "21      42.3                    42.3                        52.6   \n",
       "22      63.0                    63.0                        37.0   \n",
       "23      50.0                    50.0                        38.9   \n",
       "24      63.8                    63.8                        32.5   \n",
       "25      40.3                    40.3                        50.7   \n",
       "26      47.5                    47.5                        46.2   \n",
       "27      52.3                    52.3                        40.0   \n",
       "28      50.8                    50.8                        41.6   \n",
       "29      61.0                    61.0                        39.0   \n",
       "30      36.8                    36.8                        52.9   \n",
       "31      31.5                    31.5                        31.5   \n",
       "32      40.6                    40.6                        42.0   \n",
       "33      43.8                    43.8                        39.7   \n",
       "34      70.1                    70.1                        24.7   \n",
       "35      38.6                    38.6                        45.7   \n",
       "36      58.8                    58.8                        36.2   \n",
       "37      65.7                    65.7                        28.5   \n",
       "38      45.2                    45.2                        45.2   \n",
       "39      57.1                    57.1                        40.0   \n",
       "40      43.1                    43.1                        44.5   \n",
       "41      46.4                    46.4                        46.3   \n",
       "42      47.8                    47.8                        33.3   \n",
       "43      59.7                    59.7                        37.5   \n",
       "44      38.8                    38.8                        37.5   \n",
       "45      54.2                    54.2                        41.7   \n",
       "46      64.7                    64.7                        35.3   \n",
       "47      59.7                    59.7                        39.0   \n",
       "48      46.4                    46.4                        52.4   \n",
       "49      59.5                    59.5                        34.5   \n",
       "50      32.3                    32.3                        55.4   \n",
       "51      56.9                    56.9                        37.0   \n",
       "52      33.8                    33.8                        27.7   \n",
       "53      44.6                    44.6                        43.1   \n",
       "54      49.2                    49.2                        43.1   \n",
       "55      46.2                    46.2                        50.8   \n",
       "56      72.7                    72.7                        27.3   \n",
       "57      51.9                    51.9                        45.5   \n",
       "58      46.2                    46.2                        23.1   \n",
       "59      37.5                    37.5                        47.3   \n",
       "\n",
       "    Chance More than One Off Prof Choice  \n",
       "0                           7.300000e+00  \n",
       "1                           1.270000e+01  \n",
       "2                           1.400000e+00  \n",
       "3                           1.300000e+00  \n",
       "4                           2.600000e+00  \n",
       "5                           3.800000e+00  \n",
       "6                           9.100000e+00  \n",
       "7                           1.480000e+01  \n",
       "8                           2.360000e+01  \n",
       "9                           7.300000e+00  \n",
       "10                          7.105427e-15  \n",
       "11                          4.900000e+00  \n",
       "12                          6.500000e+00  \n",
       "13                          9.900000e+00  \n",
       "14                          6.200000e+00  \n",
       "15                          1.090000e+01  \n",
       "16                          1.200000e+00  \n",
       "17                          2.400000e+00  \n",
       "18                          2.800000e+00  \n",
       "19                          1.200000e+00  \n",
       "20                          1.070000e+01  \n",
       "21                          5.100000e+00  \n",
       "22                          0.000000e+00  \n",
       "23                          1.110000e+01  \n",
       "24                          3.700000e+00  \n",
       "25                          9.000000e+00  \n",
       "26                          6.300000e+00  \n",
       "27                          7.700000e+00  \n",
       "28                          7.600000e+00  \n",
       "29                          0.000000e+00  \n",
       "30                          1.030000e+01  \n",
       "31                          3.700000e+01  \n",
       "32                          1.740000e+01  \n",
       "33                          1.650000e+01  \n",
       "34                          5.200000e+00  \n",
       "35                          1.570000e+01  \n",
       "36                          5.000000e+00  \n",
       "37                          5.800000e+00  \n",
       "38                          9.600000e+00  \n",
       "39                          2.900000e+00  \n",
       "40                          1.240000e+01  \n",
       "41                          7.300000e+00  \n",
       "42                          1.890000e+01  \n",
       "43                          2.800000e+00  \n",
       "44                          2.370000e+01  \n",
       "45                          4.100000e+00  \n",
       "46                          0.000000e+00  \n",
       "47                          1.300000e+00  \n",
       "48                          1.200000e+00  \n",
       "49                          6.000000e+00  \n",
       "50                          1.230000e+01  \n",
       "51                          6.100000e+00  \n",
       "52                          3.850000e+01  \n",
       "53                          1.230000e+01  \n",
       "54                          7.700000e+00  \n",
       "55                          3.000000e+00  \n",
       "56                          0.000000e+00  \n",
       "57                          2.600000e+00  \n",
       "58                          3.070000e+01  \n",
       "59                          1.520000e+01  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now focus on comparison with ChatGPT and Economic Profs survey, focus on joined sample of Euo and US Profs\n",
    "togetheropinion=allclaims[allclaims['Euro/US']=='Together'].reset_index(drop=True)\n",
    "togetheropinion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7d34b616-098a-4120-bfd8-81244617364e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Claim</th>\n",
       "      <th>Strongly Agree Chatgpt</th>\n",
       "      <th>Agree Chatgpt</th>\n",
       "      <th>Disagree Chatgpt</th>\n",
       "      <th>Strongly Disagree Chatgpt</th>\n",
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       "      <th>No Opinion Chatgpt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A ban on advertising junk foods (those that ar...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A federal minimum wage that is pegged to state...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A global corporate tax system that is based on...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A global minimum corporate tax rate would limi...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>46.5</td>\n",
       "      <td>53.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A price cap imposed by the G7/EU countries on ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>As of now, there needs to be more government r...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Central banks that do not introduce their own ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Having the opportunity to work two to three da...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>High tariffs imposed by the European Union on ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>In pursuit of credible research designs, resea...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Network externalities give Twitter an incumben...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Policies that aim to reduce obesity by increas...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stablecoins that are not fully backed by eithe...</td>\n",
       "      <td>12.5</td>\n",
       "      <td>87.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Targeting the Russian economy through a total ...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Bank for International Settlements defines...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The UK economy is likely to be at least severa...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>The aggregate economy of the 27 countries stil...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The current US federal minimum wage is $7.25 p...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The economic and financial sanctions already i...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>The introduction of a central bank digital cur...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>The introduction of natural experiments to eco...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The oil price cap imposed by the G7/EU countri...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>The ‘credibility revolution’ in empirical econ...</td>\n",
       "      <td>56.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Artificial intelligence is likely to be a high...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Artificial intelligence offers substantial opp...</td>\n",
       "      <td>10.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>By enabling women’s life choices about educati...</td>\n",
       "      <td>88.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Even if Argentina could marshal the resources ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Financial regulators in the US and Europe lack...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Fiscal rules on budget deficits and public deb...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Fully guaranteeing uninsured deposits at Silic...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Gender gaps in today’s labor market arise less...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Given current regulations, non-bank financial ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>82.5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>In the absence of continuing flows of Western ...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Non-bank financial intermediaries pose a subst...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Not guaranteeing uninsured deposits at Silicon...</td>\n",
       "      <td>8.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Regulating the leverage and liquidity of non-b...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Responses To Market Power Constraints on the a...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>62.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Subsidizing Green Technology Government subsid...</td>\n",
       "      <td>59.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>The economic and financial sanctions against R...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The effectiveness of existing antitrust regime...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>The fundamental cause of Argentina’s high infl...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>The gender gap in pay would be substantially r...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would l...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would m...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>93.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Use of artificial intelligence is likely to le...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Using subsidies for green technologies instead...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Claim  Strongly Agree Chatgpt  \\\n",
       "0   A ban on advertising junk foods (those that ar...                     0.0   \n",
       "1   A federal minimum wage that is pegged to state...                     2.5   \n",
       "2   A global corporate tax system that is based on...                     0.0   \n",
       "3   A global minimum corporate tax rate would limi...                     2.0   \n",
       "4   A mandate for public companies to provide clim...                    46.5   \n",
       "5   A mandate for public companies to provide clim...                     0.0   \n",
       "6   A price cap imposed by the G7/EU countries on ...                     0.0   \n",
       "7   As of now, there needs to be more government r...                     0.0   \n",
       "8   Central banks that do not introduce their own ...                     0.0   \n",
       "9   Employees who spend two of their days each wee...                     0.0   \n",
       "10  Employees who spend two of their days each wee...                     0.5   \n",
       "11  Having the opportunity to work two to three da...                     0.0   \n",
       "12  High tariffs imposed by the European Union on ...                     0.0   \n",
       "13  In pursuit of credible research designs, resea...                     0.0   \n",
       "14  Network externalities give Twitter an incumben...                     0.0   \n",
       "15  Policies that aim to reduce obesity by increas...                     0.0   \n",
       "16  Stablecoins that are not fully backed by eithe...                    12.5   \n",
       "17  Targeting the Russian economy through a total ...                     5.5   \n",
       "18  The Bank for International Settlements defines...                     0.0   \n",
       "19  The UK economy is likely to be at least severa...                     0.5   \n",
       "20  The aggregate economy of the 27 countries stil...                     0.0   \n",
       "21  The current US federal minimum wage is $7.25 p...                     0.0   \n",
       "22  The economic and financial sanctions already i...                     1.5   \n",
       "23  The introduction of a central bank digital cur...                     0.0   \n",
       "24  The introduction of natural experiments to eco...                    22.0   \n",
       "25  The oil price cap imposed by the G7/EU countri...                     0.0   \n",
       "26  The ‘credibility revolution’ in empirical econ...                    56.0   \n",
       "27  Artificial intelligence is likely to be a high...                     0.5   \n",
       "28  Artificial intelligence offers substantial opp...                    10.5   \n",
       "29  By enabling women’s life choices about educati...                    88.5   \n",
       "30  Even if Argentina could marshal the resources ...                     1.0   \n",
       "31  Financial regulators in the US and Europe lack...                     2.5   \n",
       "32  Fiscal rules on budget deficits and public deb...                     6.0   \n",
       "33  Fully guaranteeing uninsured deposits at Silic...                     6.0   \n",
       "34  Gender gaps in today’s labor market arise less...                    10.0   \n",
       "35  Given current regulations, non-bank financial ...                     0.0   \n",
       "36  In the absence of continuing flows of Western ...                    26.0   \n",
       "37  Non-bank financial intermediaries pose a subst...                     0.5   \n",
       "38  Not guaranteeing uninsured deposits at Silicon...                     8.5   \n",
       "39  Regulating the leverage and liquidity of non-b...                     0.0   \n",
       "40  Responses To Market Power Constraints on the a...                     0.5   \n",
       "41  Since the inception of the Stability and Growt...                     1.5   \n",
       "42  Since the inception of the Stability and Growt...                     0.0   \n",
       "43  Subsidizing Green Technology Government subsid...                    59.5   \n",
       "44  The economic and financial sanctions against R...                     0.0   \n",
       "45  The effectiveness of existing antitrust regime...                    20.0   \n",
       "46  The fundamental cause of Argentina’s high infl...                     6.0   \n",
       "47  The gender gap in pay would be substantially r...                     0.0   \n",
       "48  The proposed US tariffs on Chinese EVs would l...                     0.0   \n",
       "49  The proposed US tariffs on Chinese EVs would m...                     1.0   \n",
       "50  The response to recent bank failures should be...                     0.0   \n",
       "51  The response to recent bank failures should be...                     1.0   \n",
       "52  The response to recent bank failures should be...                     0.0   \n",
       "53  Use of artificial intelligence is likely to le...                     0.5   \n",
       "54  Use of artificial intelligence over the next t...                     0.0   \n",
       "55  Use of artificial intelligence over the next t...                     0.0   \n",
       "56  Use of artificial intelligence over the next t...                     0.0   \n",
       "57  Use of artificial intelligence over the next t...                     0.0   \n",
       "58  Use of artificial intelligence over the next t...                     0.0   \n",
       "59  Using subsidies for green technologies instead...                     1.5   \n",
       "\n",
       "    Agree Chatgpt  Disagree Chatgpt  Strongly Disagree Chatgpt  \\\n",
       "0           100.0               0.0                        0.0   \n",
       "1            96.0               0.0                        0.0   \n",
       "2            25.5               0.0                        0.0   \n",
       "3            98.0               0.0                        0.0   \n",
       "4            53.5               0.0                        0.0   \n",
       "5            90.5               0.0                        0.0   \n",
       "6            45.5               0.0                        0.0   \n",
       "7            98.5               0.0                        0.0   \n",
       "8            38.0               0.0                        0.0   \n",
       "9           100.0               0.0                        0.0   \n",
       "10           99.5               0.0                        0.0   \n",
       "11           16.0               0.0                        0.0   \n",
       "12            0.5               0.5                        0.0   \n",
       "13            7.0              82.0                       10.5   \n",
       "14          100.0               0.0                        0.0   \n",
       "15           41.0               0.0                        0.0   \n",
       "16           87.5               0.0                        0.0   \n",
       "17           94.5               0.0                        0.0   \n",
       "18           95.5               0.0                        0.0   \n",
       "19           96.5               0.0                        0.0   \n",
       "20            0.0               0.0                        0.0   \n",
       "21            0.0               0.0                        0.0   \n",
       "22           91.5               0.0                        0.0   \n",
       "23            0.0              99.0                        1.0   \n",
       "24           78.0               0.0                        0.0   \n",
       "25           26.0               0.0                        0.0   \n",
       "26           44.0               0.0                        0.0   \n",
       "27           99.5               0.0                        0.0   \n",
       "28           89.5               0.0                        0.0   \n",
       "29           11.5               0.0                        0.0   \n",
       "30           53.0               0.0                        0.0   \n",
       "31           22.5               1.0                        0.0   \n",
       "32           94.0               0.0                        0.0   \n",
       "33           94.0               0.0                        0.0   \n",
       "34           90.0               0.0                        0.0   \n",
       "35            7.5               8.0                        0.0   \n",
       "36           74.0               0.0                        0.0   \n",
       "37           38.0               0.0                        0.0   \n",
       "38           74.5               0.0                        0.0   \n",
       "39          100.0               0.0                        0.0   \n",
       "40           98.5               0.0                        0.0   \n",
       "41           62.5               0.0                        0.0   \n",
       "42            1.0               0.0                        0.0   \n",
       "43           40.5               0.0                        0.0   \n",
       "44           27.0               0.0                        0.0   \n",
       "45           80.0               0.0                        0.0   \n",
       "46           94.0               0.0                        0.0   \n",
       "47          100.0               0.0                        0.0   \n",
       "48            0.0               0.0                        0.0   \n",
       "49           95.0               0.0                        0.0   \n",
       "50           99.0               0.0                        0.0   \n",
       "51           99.0               0.0                        0.0   \n",
       "52            6.5               0.0                        0.0   \n",
       "53           99.5               0.0                        0.0   \n",
       "54          100.0               0.0                        0.0   \n",
       "55            0.0               0.0                        0.0   \n",
       "56           12.0               0.0                        0.0   \n",
       "57           21.5               0.0                        0.0   \n",
       "58           17.5               1.0                        0.5   \n",
       "59           98.5               0.0                        0.0   \n",
       "\n",
       "    Uncertain Chatgpt  No Opinion Chatgpt  \n",
       "0                 0.0                   0  \n",
       "1                 1.5                   0  \n",
       "2                74.5                   0  \n",
       "3                 0.0                   0  \n",
       "4                 0.0                   0  \n",
       "5                 9.5                   0  \n",
       "6                54.5                   0  \n",
       "7                 1.5                   0  \n",
       "8                62.0                   0  \n",
       "9                 0.0                   0  \n",
       "10                0.0                   0  \n",
       "11               84.0                   0  \n",
       "12               99.0                   0  \n",
       "13                0.5                   0  \n",
       "14                0.0                   0  \n",
       "15               59.0                   0  \n",
       "16                0.0                   0  \n",
       "17                0.0                   0  \n",
       "18                4.5                   0  \n",
       "19                3.0                   0  \n",
       "20              100.0                   0  \n",
       "21              100.0                   0  \n",
       "22                7.0                   0  \n",
       "23                0.0                   0  \n",
       "24                0.0                   0  \n",
       "25               74.0                   0  \n",
       "26                0.0                   0  \n",
       "27                0.0                   0  \n",
       "28                0.0                   0  \n",
       "29                0.0                   0  \n",
       "30               46.0                   0  \n",
       "31               74.0                   0  \n",
       "32                0.0                   0  \n",
       "33                0.0                   0  \n",
       "34                0.0                   0  \n",
       "35               82.5                   2  \n",
       "36                0.0                   0  \n",
       "37               61.5                   0  \n",
       "38               17.0                   0  \n",
       "39                0.0                   0  \n",
       "40                1.0                   0  \n",
       "41               36.0                   0  \n",
       "42               99.0                   0  \n",
       "43                0.0                   0  \n",
       "44               73.0                   0  \n",
       "45                0.0                   0  \n",
       "46                0.0                   0  \n",
       "47                0.0                   0  \n",
       "48              100.0                   0  \n",
       "49                4.0                   0  \n",
       "50                1.0                   0  \n",
       "51                0.0                   0  \n",
       "52               93.5                   0  \n",
       "53                0.0                   0  \n",
       "54                0.0                   0  \n",
       "55              100.0                   0  \n",
       "56               88.0                   0  \n",
       "57               78.5                   0  \n",
       "58               81.0                   0  \n",
       "59                0.0                   0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Claim</th>\n",
       "      <th>Strongly Agree Chatgpt</th>\n",
       "      <th>Agree Chatgpt</th>\n",
       "      <th>Disagree Chatgpt</th>\n",
       "      <th>Strongly Disagree Chatgpt</th>\n",
       "      <th>Uncertain Chatgpt</th>\n",
       "      <th>No Opinion Chatgpt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A ban on advertising junk foods (those that ar...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A federal minimum wage that is pegged to state...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A global corporate tax system that is based on...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A global minimum corporate tax rate would limi...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>72.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A price cap imposed by the G7/EU countries on ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>As of now, there needs to be more government r...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Central banks that do not introduce their own ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Having the opportunity to work two to three da...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>High tariffs imposed by the European Union on ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>In pursuit of credible research designs, resea...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>18</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Network externalities give Twitter an incumben...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Policies that aim to reduce obesity by increas...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stablecoins that are not fully backed by eithe...</td>\n",
       "      <td>59.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Targeting the Russian economy through a total ...</td>\n",
       "      <td>29.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Bank for International Settlements defines...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The UK economy is likely to be at least severa...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>The aggregate economy of the 27 countries stil...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The current US federal minimum wage is $7.25 p...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The economic and financial sanctions already i...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>The introduction of a central bank digital cur...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>The introduction of natural experiments to eco...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The oil price cap imposed by the G7/EU countri...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>The ‘credibility revolution’ in empirical econ...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Artificial intelligence is likely to be a high...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Artificial intelligence offers substantial opp...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>By enabling women’s life choices about educati...</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Even if Argentina could marshal the resources ...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Financial regulators in the US and Europe lack...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Fiscal rules on budget deficits and public deb...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Fully guaranteeing uninsured deposits at Silic...</td>\n",
       "      <td>8.5</td>\n",
       "      <td>91.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Gender gaps in today’s labor market arise less...</td>\n",
       "      <td>41.5</td>\n",
       "      <td>58.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Given current regulations, non-bank financial ...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>In the absence of continuing flows of Western ...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Non-bank financial intermediaries pose a subst...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Not guaranteeing uninsured deposits at Silicon...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Regulating the leverage and liquidity of non-b...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Responses To Market Power Constraints on the a...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Subsidizing Green Technology Government subsid...</td>\n",
       "      <td>65.5</td>\n",
       "      <td>34.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>The economic and financial sanctions against R...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The effectiveness of existing antitrust regime...</td>\n",
       "      <td>16.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>The fundamental cause of Argentina’s high infl...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>The gender gap in pay would be substantially r...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would l...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would m...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Use of artificial intelligence is likely to le...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Using subsidies for green technologies instead...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Claim  Strongly Agree Chatgpt  \\\n",
       "0   A ban on advertising junk foods (those that ar...                     0.0   \n",
       "1   A federal minimum wage that is pegged to state...                     0.0   \n",
       "2   A global corporate tax system that is based on...                     0.5   \n",
       "3   A global minimum corporate tax rate would limi...                     2.0   \n",
       "4   A mandate for public companies to provide clim...                    72.5   \n",
       "5   A mandate for public companies to provide clim...                     0.0   \n",
       "6   A price cap imposed by the G7/EU countries on ...                     0.0   \n",
       "7   As of now, there needs to be more government r...                     4.5   \n",
       "8   Central banks that do not introduce their own ...                     0.0   \n",
       "9   Employees who spend two of their days each wee...                     0.0   \n",
       "10  Employees who spend two of their days each wee...                     1.5   \n",
       "11  Having the opportunity to work two to three da...                     0.5   \n",
       "12  High tariffs imposed by the European Union on ...                     0.0   \n",
       "13  In pursuit of credible research designs, resea...                     0.0   \n",
       "14  Network externalities give Twitter an incumben...                     2.5   \n",
       "15  Policies that aim to reduce obesity by increas...                     0.0   \n",
       "16  Stablecoins that are not fully backed by eithe...                    59.5   \n",
       "17  Targeting the Russian economy through a total ...                    29.5   \n",
       "18  The Bank for International Settlements defines...                     0.0   \n",
       "19  The UK economy is likely to be at least severa...                     0.0   \n",
       "20  The aggregate economy of the 27 countries stil...                     0.5   \n",
       "21  The current US federal minimum wage is $7.25 p...                     0.0   \n",
       "22  The economic and financial sanctions already i...                     0.5   \n",
       "23  The introduction of a central bank digital cur...                     0.0   \n",
       "24  The introduction of natural experiments to eco...                    91.0   \n",
       "25  The oil price cap imposed by the G7/EU countri...                     0.0   \n",
       "26  The ‘credibility revolution’ in empirical econ...                    99.0   \n",
       "27  Artificial intelligence is likely to be a high...                     3.5   \n",
       "28  Artificial intelligence offers substantial opp...                     3.0   \n",
       "29  By enabling women’s life choices about educati...                    99.0   \n",
       "30  Even if Argentina could marshal the resources ...                     3.5   \n",
       "31  Financial regulators in the US and Europe lack...                     0.0   \n",
       "32  Fiscal rules on budget deficits and public deb...                     2.5   \n",
       "33  Fully guaranteeing uninsured deposits at Silic...                     8.5   \n",
       "34  Gender gaps in today’s labor market arise less...                    41.5   \n",
       "35  Given current regulations, non-bank financial ...                     0.0   \n",
       "36  In the absence of continuing flows of Western ...                    78.0   \n",
       "37  Non-bank financial intermediaries pose a subst...                     0.0   \n",
       "38  Not guaranteeing uninsured deposits at Silicon...                     2.5   \n",
       "39  Regulating the leverage and liquidity of non-b...                     0.5   \n",
       "40  Responses To Market Power Constraints on the a...                     0.5   \n",
       "41  Since the inception of the Stability and Growt...                     0.0   \n",
       "42  Since the inception of the Stability and Growt...                     0.0   \n",
       "43  Subsidizing Green Technology Government subsid...                    65.5   \n",
       "44  The economic and financial sanctions against R...                     0.0   \n",
       "45  The effectiveness of existing antitrust regime...                    16.5   \n",
       "46  The fundamental cause of Argentina’s high infl...                    22.0   \n",
       "47  The gender gap in pay would be substantially r...                    10.0   \n",
       "48  The proposed US tariffs on Chinese EVs would l...                     0.0   \n",
       "49  The proposed US tariffs on Chinese EVs would m...                     0.0   \n",
       "50  The response to recent bank failures should be...                     0.0   \n",
       "51  The response to recent bank failures should be...                     1.0   \n",
       "52  The response to recent bank failures should be...                     0.0   \n",
       "53  Use of artificial intelligence is likely to le...                     1.5   \n",
       "54  Use of artificial intelligence over the next t...                     2.5   \n",
       "55  Use of artificial intelligence over the next t...                     0.0   \n",
       "56  Use of artificial intelligence over the next t...                     0.0   \n",
       "57  Use of artificial intelligence over the next t...                     0.0   \n",
       "58  Use of artificial intelligence over the next t...                     2.5   \n",
       "59  Using subsidies for green technologies instead...                     3.5   \n",
       "\n",
       "    Agree Chatgpt  Disagree Chatgpt  Strongly Disagree Chatgpt  \\\n",
       "0            59.5               0.0                          0   \n",
       "1            99.0               0.0                          0   \n",
       "2            44.5               0.0                          0   \n",
       "3            98.0               0.0                          0   \n",
       "4            27.5               0.0                          0   \n",
       "5            80.5               0.0                          0   \n",
       "6            69.0               0.0                          0   \n",
       "7            60.5               0.0                          0   \n",
       "8            18.0               0.5                          0   \n",
       "9            88.5               0.0                          0   \n",
       "10           98.5               0.0                          0   \n",
       "11           32.5               0.0                          0   \n",
       "12            0.5               9.5                          0   \n",
       "13            2.0              79.5                         18   \n",
       "14           97.5               0.0                          0   \n",
       "15           35.0               0.0                          0   \n",
       "16           40.5               0.0                          0   \n",
       "17           70.5               0.0                          0   \n",
       "18           74.5               0.0                          0   \n",
       "19          100.0               0.0                          0   \n",
       "20           23.5               0.0                          0   \n",
       "21            0.0               0.0                          0   \n",
       "22           99.5               0.0                          0   \n",
       "23            0.0              99.5                          0   \n",
       "24            9.0               0.0                          0   \n",
       "25            9.0               4.5                          0   \n",
       "26            1.0               0.0                          0   \n",
       "27           96.5               0.0                          0   \n",
       "28           97.0               0.0                          0   \n",
       "29            1.0               0.0                          0   \n",
       "30           78.5               2.5                          0   \n",
       "31           14.5              15.5                          2   \n",
       "32           97.5               0.0                          0   \n",
       "33           91.5               0.0                          0   \n",
       "34           58.5               0.0                          0   \n",
       "35            1.0              41.5                          0   \n",
       "36           22.0               0.0                          0   \n",
       "37           75.5               0.0                          0   \n",
       "38           78.0               0.0                          0   \n",
       "39           99.5               0.0                          0   \n",
       "40           92.0               0.0                          0   \n",
       "41           86.5               0.0                          0   \n",
       "42            1.0               0.0                          0   \n",
       "43           34.5               0.0                          0   \n",
       "44           32.0               0.0                          0   \n",
       "45           83.5               0.0                          0   \n",
       "46           78.0               0.0                          0   \n",
       "47           90.0               0.0                          0   \n",
       "48            0.0               4.5                          0   \n",
       "49           95.0               0.0                          0   \n",
       "50          100.0               0.0                          0   \n",
       "51           99.0               0.0                          0   \n",
       "52            2.5              12.5                          0   \n",
       "53           98.5               0.0                          0   \n",
       "54           97.5               0.0                          0   \n",
       "55            0.0               3.0                          0   \n",
       "56           28.0               0.0                          0   \n",
       "57            5.0               0.0                          0   \n",
       "58           73.0               2.0                          0   \n",
       "59           96.5               0.0                          0   \n",
       "\n",
       "    Uncertain Chatgpt  No Opinion Chatgpt  \n",
       "0                40.5                 0.0  \n",
       "1                 1.0                 0.0  \n",
       "2                55.0                 0.0  \n",
       "3                 0.0                 0.0  \n",
       "4                 0.0                 0.0  \n",
       "5                19.5                 0.0  \n",
       "6                31.0                 0.0  \n",
       "7                34.5                 0.5  \n",
       "8                81.5                 0.0  \n",
       "9                11.5                 0.0  \n",
       "10                0.0                 0.0  \n",
       "11               67.0                 0.0  \n",
       "12               90.0                 0.0  \n",
       "13                0.5                 0.0  \n",
       "14                0.0                 0.0  \n",
       "15               65.0                 0.0  \n",
       "16                0.0                 0.0  \n",
       "17                0.0                 0.0  \n",
       "18               25.5                 0.0  \n",
       "19                0.0                 0.0  \n",
       "20               76.0                 0.0  \n",
       "21              100.0                 0.0  \n",
       "22                0.0                 0.0  \n",
       "23                0.5                 0.0  \n",
       "24                0.0                 0.0  \n",
       "25               86.5                 0.0  \n",
       "26                0.0                 0.0  \n",
       "27                0.0                 0.0  \n",
       "28                0.0                 0.0  \n",
       "29                0.0                 0.0  \n",
       "30               15.5                 0.0  \n",
       "31               68.0                 0.0  \n",
       "32                0.0                 0.0  \n",
       "33                0.0                 0.0  \n",
       "34                0.0                 0.0  \n",
       "35               57.5                 0.0  \n",
       "36                0.0                 0.0  \n",
       "37               24.5                 0.0  \n",
       "38               19.5                 0.0  \n",
       "39                0.0                 0.0  \n",
       "40                7.5                 0.0  \n",
       "41               13.5                 0.0  \n",
       "42               99.0                 0.0  \n",
       "43                0.0                 0.0  \n",
       "44               68.0                 0.0  \n",
       "45                0.0                 0.0  \n",
       "46                0.0                 0.0  \n",
       "47                0.0                 0.0  \n",
       "48               95.5                 0.0  \n",
       "49                5.0                 0.0  \n",
       "50                0.0                 0.0  \n",
       "51                0.0                 0.0  \n",
       "52               85.0                 0.0  \n",
       "53                0.0                 0.0  \n",
       "54                0.0                 0.0  \n",
       "55               97.0                 0.0  \n",
       "56               72.0                 0.0  \n",
       "57               95.0                 0.0  \n",
       "58               22.5                 0.0  \n",
       "59                0.0                 0.0  "
      ]
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       "      <th>Claim</th>\n",
       "      <th>Strongly Agree Chatgpt</th>\n",
       "      <th>Agree Chatgpt</th>\n",
       "      <th>Disagree Chatgpt</th>\n",
       "      <th>Strongly Disagree Chatgpt</th>\n",
       "      <th>Uncertain Chatgpt</th>\n",
       "      <th>No Opinion Chatgpt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A ban on advertising junk foods (those that ar...</td>\n",
       "      <td>59.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A federal minimum wage that is pegged to state...</td>\n",
       "      <td>48.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A global corporate tax system that is based on...</td>\n",
       "      <td>50.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A global minimum corporate tax rate would limi...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>60.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>72.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A price cap imposed by the G7/EU countries on ...</td>\n",
       "      <td>43.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>As of now, there needs to be more government r...</td>\n",
       "      <td>46.5</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Central banks that do not introduce their own ...</td>\n",
       "      <td>37.5</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Having the opportunity to work two to three da...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>High tariffs imposed by the European Union on ...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>In pursuit of credible research designs, resea...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Network externalities give Twitter an incumben...</td>\n",
       "      <td>46.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Policies that aim to reduce obesity by increas...</td>\n",
       "      <td>43.5</td>\n",
       "      <td>56.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Stablecoins that are not fully backed by eithe...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Targeting the Russian economy through a total ...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>The Bank for International Settlements defines...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>The UK economy is likely to be at least severa...</td>\n",
       "      <td>30.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>The aggregate economy of the 27 countries stil...</td>\n",
       "      <td>34.0</td>\n",
       "      <td>61.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>The current US federal minimum wage is $7.25 p...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>86.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>The economic and financial sanctions already i...</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>The introduction of a central bank digital cur...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>22.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>The introduction of natural experiments to eco...</td>\n",
       "      <td>66.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>The oil price cap imposed by the G7/EU countri...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>The ‘credibility revolution’ in empirical econ...</td>\n",
       "      <td>56.5</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Artificial intelligence is likely to be a high...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Artificial intelligence offers substantial opp...</td>\n",
       "      <td>43.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>By enabling women’s life choices about educati...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Even if Argentina could marshal the resources ...</td>\n",
       "      <td>10.5</td>\n",
       "      <td>33.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Financial regulators in the US and Europe lack...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>35.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Fiscal rules on budget deficits and public deb...</td>\n",
       "      <td>44.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Fully guaranteeing uninsured deposits at Silic...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Gender gaps in today’s labor market arise less...</td>\n",
       "      <td>31.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Given current regulations, non-bank financial ...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>In the absence of continuing flows of Western ...</td>\n",
       "      <td>26.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Non-bank financial intermediaries pose a subst...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Not guaranteeing uninsured deposits at Silicon...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Regulating the leverage and liquidity of non-b...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Responses To Market Power Constraints on the a...</td>\n",
       "      <td>19.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>47.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>29.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Subsidizing Green Technology Government subsid...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>The economic and financial sanctions against R...</td>\n",
       "      <td>62.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>The effectiveness of existing antitrust regime...</td>\n",
       "      <td>55.5</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>The fundamental cause of Argentina’s high infl...</td>\n",
       "      <td>76.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>The gender gap in pay would be substantially r...</td>\n",
       "      <td>36.5</td>\n",
       "      <td>63.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would l...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>The proposed US tariffs on Chinese EVs would m...</td>\n",
       "      <td>28.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>37.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>27.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Use of artificial intelligence is likely to le...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>33.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>73.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>57.5</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Using subsidies for green technologies instead...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                Claim  Strongly Agree Chatgpt  \\\n",
       "0   A ban on advertising junk foods (those that ar...                    59.5   \n",
       "1   A federal minimum wage that is pegged to state...                    48.5   \n",
       "2   A global corporate tax system that is based on...                    50.0   \n",
       "3   A global minimum corporate tax rate would limi...                    31.0   \n",
       "4   A mandate for public companies to provide clim...                    60.5   \n",
       "5   A mandate for public companies to provide clim...                    72.5   \n",
       "6   A price cap imposed by the G7/EU countries on ...                    43.5   \n",
       "7   As of now, there needs to be more government r...                    46.5   \n",
       "8   Central banks that do not introduce their own ...                    37.5   \n",
       "9   Employees who spend two of their days each wee...                    30.0   \n",
       "10  Employees who spend two of their days each wee...                    13.0   \n",
       "11  Having the opportunity to work two to three da...                     7.0   \n",
       "12  High tariffs imposed by the European Union on ...                    13.0   \n",
       "13  In pursuit of credible research designs, resea...                     2.0   \n",
       "14  Network externalities give Twitter an incumben...                    46.0   \n",
       "15  Policies that aim to reduce obesity by increas...                    43.5   \n",
       "16  Stablecoins that are not fully backed by eithe...                    30.0   \n",
       "17  Targeting the Russian economy through a total ...                    21.0   \n",
       "18  The Bank for International Settlements defines...                    27.0   \n",
       "19  The UK economy is likely to be at least severa...                    30.5   \n",
       "20  The aggregate economy of the 27 countries stil...                    34.0   \n",
       "21  The current US federal minimum wage is $7.25 p...                     0.0   \n",
       "22  The economic and financial sanctions already i...                    11.0   \n",
       "23  The introduction of a central bank digital cur...                     0.0   \n",
       "24  The introduction of natural experiments to eco...                    66.0   \n",
       "25  The oil price cap imposed by the G7/EU countri...                     2.0   \n",
       "26  The ‘credibility revolution’ in empirical econ...                    56.5   \n",
       "27  Artificial intelligence is likely to be a high...                    42.0   \n",
       "28  Artificial intelligence offers substantial opp...                    43.5   \n",
       "29  By enabling women’s life choices about educati...                    72.0   \n",
       "30  Even if Argentina could marshal the resources ...                    10.5   \n",
       "31  Financial regulators in the US and Europe lack...                    31.0   \n",
       "32  Fiscal rules on budget deficits and public deb...                    44.0   \n",
       "33  Fully guaranteeing uninsured deposits at Silic...                     0.5   \n",
       "34  Gender gaps in today’s labor market arise less...                    31.0   \n",
       "35  Given current regulations, non-bank financial ...                    14.0   \n",
       "36  In the absence of continuing flows of Western ...                    26.5   \n",
       "37  Non-bank financial intermediaries pose a subst...                     0.5   \n",
       "38  Not guaranteeing uninsured deposits at Silicon...                    82.0   \n",
       "39  Regulating the leverage and liquidity of non-b...                    30.0   \n",
       "40  Responses To Market Power Constraints on the a...                    19.5   \n",
       "41  Since the inception of the Stability and Growt...                    47.5   \n",
       "42  Since the inception of the Stability and Growt...                    29.0   \n",
       "43  Subsidizing Green Technology Government subsid...                    32.0   \n",
       "44  The economic and financial sanctions against R...                    62.5   \n",
       "45  The effectiveness of existing antitrust regime...                    55.5   \n",
       "46  The fundamental cause of Argentina’s high infl...                    76.0   \n",
       "47  The gender gap in pay would be substantially r...                    36.5   \n",
       "48  The proposed US tariffs on Chinese EVs would l...                     0.0   \n",
       "49  The proposed US tariffs on Chinese EVs would m...                    28.5   \n",
       "50  The response to recent bank failures should be...                    37.0   \n",
       "51  The response to recent bank failures should be...                    74.0   \n",
       "52  The response to recent bank failures should be...                    27.0   \n",
       "53  Use of artificial intelligence is likely to le...                    30.0   \n",
       "54  Use of artificial intelligence over the next t...                     8.0   \n",
       "55  Use of artificial intelligence over the next t...                     1.0   \n",
       "56  Use of artificial intelligence over the next t...                    73.0   \n",
       "57  Use of artificial intelligence over the next t...                    57.5   \n",
       "58  Use of artificial intelligence over the next t...                     7.0   \n",
       "59  Using subsidies for green technologies instead...                     8.0   \n",
       "\n",
       "    Agree Chatgpt  Disagree Chatgpt  Strongly Disagree Chatgpt  \\\n",
       "0            40.5               0.0                        0.0   \n",
       "1            48.5               1.0                        1.0   \n",
       "2            47.5               0.0                        2.5   \n",
       "3            68.5               0.5                        0.0   \n",
       "4            39.5               0.0                        0.0   \n",
       "5            27.5               0.0                        0.0   \n",
       "6            55.5               1.0                        0.0   \n",
       "7            53.0               0.0                        0.0   \n",
       "8            56.5               0.5                        4.5   \n",
       "9            69.5               0.0                        0.0   \n",
       "10           86.5               0.0                        0.0   \n",
       "11           44.0               8.0                       38.5   \n",
       "12           84.0               0.5                        1.0   \n",
       "13           25.5              12.5                       58.0   \n",
       "14           29.0               2.5                       22.0   \n",
       "15           56.5               0.0                        0.0   \n",
       "16           45.5               5.5                       16.5   \n",
       "17           46.5              10.0                       20.5   \n",
       "18           72.5               0.0                        0.0   \n",
       "19           47.5               1.0                       14.5   \n",
       "20           61.5               0.5                        3.0   \n",
       "21            2.0              10.5                       86.5   \n",
       "22           21.5               6.0                       57.5   \n",
       "23           12.0              60.5                       22.5   \n",
       "24           34.0               0.0                        0.0   \n",
       "25           16.0              38.5                       41.0   \n",
       "26           43.5               0.0                        0.0   \n",
       "27           45.5               2.5                        9.5   \n",
       "28           55.5               0.0                        0.0   \n",
       "29           28.0               0.0                        0.0   \n",
       "30           33.0              16.5                       38.5   \n",
       "31           35.5               6.0                       25.0   \n",
       "32           56.0               0.0                        0.0   \n",
       "33            4.0              15.0                       80.5   \n",
       "34           69.0               0.0                        0.0   \n",
       "35           17.0              17.5                       50.0   \n",
       "36           28.5               1.0                       43.0   \n",
       "37            8.0              54.0                       31.0   \n",
       "38            7.5               1.0                        9.5   \n",
       "39           69.5               0.0                        0.0   \n",
       "40           80.0               0.0                        0.5   \n",
       "41           52.5               0.0                        0.0   \n",
       "42           69.0               0.0                        1.0   \n",
       "43           68.0               0.0                        0.0   \n",
       "44           37.5               0.0                        0.0   \n",
       "45           44.0               0.0                        0.0   \n",
       "46           22.5               0.5                        1.0   \n",
       "47           63.5               0.0                        0.0   \n",
       "48           71.0               2.5                       14.0   \n",
       "49           13.5              12.0                       46.0   \n",
       "50           61.0               0.0                        0.5   \n",
       "51           25.5               0.0                        0.0   \n",
       "52           23.0               0.5                       47.5   \n",
       "53           52.0               9.0                        5.0   \n",
       "54           35.0              13.0                       33.5   \n",
       "55           15.0              28.0                       53.0   \n",
       "56           26.5               0.0                        0.5   \n",
       "57           41.5               0.0                        0.5   \n",
       "58           20.5               3.5                       16.5   \n",
       "59           23.0               8.5                       60.5   \n",
       "\n",
       "    Uncertain Chatgpt  No Opinion Chatgpt  \n",
       "0                 0.0                   0  \n",
       "1                 1.0                   0  \n",
       "2                 0.0                   0  \n",
       "3                 0.0                   0  \n",
       "4                 0.0                   0  \n",
       "5                 0.0                   0  \n",
       "6                 0.0                   0  \n",
       "7                 0.5                   0  \n",
       "8                 1.0                   0  \n",
       "9                 0.5                   0  \n",
       "10                0.5                   0  \n",
       "11                2.5                   0  \n",
       "12                1.5                   0  \n",
       "13                2.0                   0  \n",
       "14                0.5                   0  \n",
       "15                0.0                   0  \n",
       "16                2.5                   0  \n",
       "17                2.0                   0  \n",
       "18                0.5                   0  \n",
       "19                6.5                   0  \n",
       "20                1.0                   0  \n",
       "21                1.0                   0  \n",
       "22                4.0                   0  \n",
       "23                5.0                   0  \n",
       "24                0.0                   0  \n",
       "25                2.5                   0  \n",
       "26                0.0                   0  \n",
       "27                0.5                   0  \n",
       "28                1.0                   0  \n",
       "29                0.0                   0  \n",
       "30                1.5                   0  \n",
       "31                2.5                   0  \n",
       "32                0.0                   0  \n",
       "33                0.0                   0  \n",
       "34                0.0                   0  \n",
       "35                1.5                   0  \n",
       "36                1.0                   0  \n",
       "37                6.5                   0  \n",
       "38                0.0                   0  \n",
       "39                0.5                   0  \n",
       "40                0.0                   0  \n",
       "41                0.0                   0  \n",
       "42                1.0                   0  \n",
       "43                0.0                   0  \n",
       "44                0.0                   0  \n",
       "45                0.5                   0  \n",
       "46                0.0                   0  \n",
       "47                0.0                   0  \n",
       "48               12.5                   0  \n",
       "49                0.0                   0  \n",
       "50                1.5                   0  \n",
       "51                0.5                   0  \n",
       "52                2.0                   0  \n",
       "53                4.0                   0  \n",
       "54               10.5                   0  \n",
       "55                3.0                   0  \n",
       "56                0.0                   0  \n",
       "57                0.5                   0  \n",
       "58               52.5                   0  \n",
       "59                0.0                   0  "
      ]
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       "      <th></th>\n",
       "      <th>Euro/US</th>\n",
       "      <th>Claim</th>\n",
       "      <th>Strongly Agree</th>\n",
       "      <th>Agree</th>\n",
       "      <th>Disagree</th>\n",
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       "      <th>...</th>\n",
       "      <th>Chance More than One Off Prof Choice</th>\n",
       "      <th>Chance GPT4o Top Prof Choice</th>\n",
       "      <th>Chance GPT35 Top Prof Choice</th>\n",
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       "      <th>Chance GPT4o More than One Off Top Prof Choice</th>\n",
       "      <th>Chance GPT4oProf One Off Top Prof Choice</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Together</td>\n",
       "      <td>A ban on advertising junk foods (those that ar...</td>\n",
       "      <td>3.7</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>6.1</td>\n",
       "      <td>82</td>\n",
       "      <td>61.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Together</td>\n",
       "      <td>A federal minimum wage that is pegged to state...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>43.6</td>\n",
       "      <td>12.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>78</td>\n",
       "      <td>65.4</td>\n",
       "      <td>...</td>\n",
       "      <td>1.270000e+01</td>\n",
       "      <td>96.0</td>\n",
       "      <td>48.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Together</td>\n",
       "      <td>A global corporate tax system that is based on...</td>\n",
       "      <td>9.3</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75</td>\n",
       "      <td>54.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.400000e+00</td>\n",
       "      <td>25.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Together</td>\n",
       "      <td>A global minimum corporate tax rate would limi...</td>\n",
       "      <td>22.7</td>\n",
       "      <td>66.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>75</td>\n",
       "      <td>90.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.300000e+00</td>\n",
       "      <td>98.0</td>\n",
       "      <td>68.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Together</td>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>18.2</td>\n",
       "      <td>63.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>84.4</td>\n",
       "      <td>...</td>\n",
       "      <td>2.600000e+00</td>\n",
       "      <td>53.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Together</td>\n",
       "      <td>A mandate for public companies to provide clim...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>49.4</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.3</td>\n",
       "      <td>42.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>55.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3.800000e+00</td>\n",
       "      <td>90.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Together</td>\n",
       "      <td>A price cap imposed by the G7/EU countries on ...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>51.9</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>28.6</td>\n",
       "      <td>3.9</td>\n",
       "      <td>77</td>\n",
       "      <td>67.5</td>\n",
       "      <td>...</td>\n",
       "      <td>9.100000e+00</td>\n",
       "      <td>45.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>69.0</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Together</td>\n",
       "      <td>As of now, there needs to be more government r...</td>\n",
       "      <td>14.8</td>\n",
       "      <td>42.0</td>\n",
       "      <td>9.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>27.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>81</td>\n",
       "      <td>71.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.480000e+01</td>\n",
       "      <td>98.5</td>\n",
       "      <td>53.0</td>\n",
       "      <td>60.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Together</td>\n",
       "      <td>Central banks that do not introduce their own ...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>22.2</td>\n",
       "      <td>31.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>27.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>72</td>\n",
       "      <td>58.3</td>\n",
       "      <td>...</td>\n",
       "      <td>2.360000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Together</td>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>26.8</td>\n",
       "      <td>12.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>82</td>\n",
       "      <td>41.5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Together</td>\n",
       "      <td>Employees who spend two of their days each wee...</td>\n",
       "      <td>4.9</td>\n",
       "      <td>57.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.9</td>\n",
       "      <td>4.9</td>\n",
       "      <td>82</td>\n",
       "      <td>62.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.105427e-15</td>\n",
       "      <td>99.5</td>\n",
       "      <td>86.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Together</td>\n",
       "      <td>Having the opportunity to work two to three da...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>25.6</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1.2</td>\n",
       "      <td>54.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>82</td>\n",
       "      <td>42.7</td>\n",
       "      <td>...</td>\n",
       "      <td>4.900000e+00</td>\n",
       "      <td>84.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>52.0</td>\n",
       "      <td>45.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Together</td>\n",
       "      <td>High tariffs imposed by the European Union on ...</td>\n",
       "      <td>11.7</td>\n",
       "      <td>54.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>77</td>\n",
       "      <td>72.7</td>\n",
       "      <td>...</td>\n",
       "      <td>6.500000e+00</td>\n",
       "      <td>0.5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Together</td>\n",
       "      <td>In pursuit of credible research designs, resea...</td>\n",
       "      <td>8.8</td>\n",
       "      <td>42.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>33.8</td>\n",
       "      <td>5.0</td>\n",
       "      <td>80</td>\n",
       "      <td>61.2</td>\n",
       "      <td>...</td>\n",
       "      <td>9.900000e+00</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>92.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>70.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Together</td>\n",
       "      <td>Network externalities give Twitter an incumben...</td>\n",
       "      <td>29.6</td>\n",
       "      <td>56.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.5</td>\n",
       "      <td>81</td>\n",
       "      <td>92.6</td>\n",
       "      <td>...</td>\n",
       "      <td>6.200000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.5</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Together</td>\n",
       "      <td>Policies that aim to reduce obesity by increas...</td>\n",
       "      <td>2.4</td>\n",
       "      <td>22.0</td>\n",
       "      <td>15.9</td>\n",
       "      <td>2.4</td>\n",
       "      <td>51.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>82</td>\n",
       "      <td>42.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.090000e+01</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Together</td>\n",
       "      <td>Stablecoins that are not fully backed by eithe...</td>\n",
       "      <td>38.7</td>\n",
       "      <td>50.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.7</td>\n",
       "      <td>6.7</td>\n",
       "      <td>75</td>\n",
       "      <td>90.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>87.5</td>\n",
       "      <td>45.5</td>\n",
       "      <td>40.5</td>\n",
       "      <td>12.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Together</td>\n",
       "      <td>Targeting the Russian economy through a total ...</td>\n",
       "      <td>11.1</td>\n",
       "      <td>49.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>81</td>\n",
       "      <td>63.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.400000e+00</td>\n",
       "      <td>94.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>70.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>30.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Together</td>\n",
       "      <td>The Bank for International Settlements defines...</td>\n",
       "      <td>9.7</td>\n",
       "      <td>40.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.3</td>\n",
       "      <td>13.9</td>\n",
       "      <td>72</td>\n",
       "      <td>52.8</td>\n",
       "      <td>...</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>95.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Together</td>\n",
       "      <td>The UK economy is likely to be at least severa...</td>\n",
       "      <td>23.8</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.4</td>\n",
       "      <td>84</td>\n",
       "      <td>82.1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>96.5</td>\n",
       "      <td>47.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>15.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Together</td>\n",
       "      <td>The aggregate economy of the 27 countries stil...</td>\n",
       "      <td>4.8</td>\n",
       "      <td>15.5</td>\n",
       "      <td>32.1</td>\n",
       "      <td>3.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>2.4</td>\n",
       "      <td>84</td>\n",
       "      <td>56.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.070000e+01</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Together</td>\n",
       "      <td>The current US federal minimum wage is $7.25 p...</td>\n",
       "      <td>2.6</td>\n",
       "      <td>35.9</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>2.6</td>\n",
       "      <td>78</td>\n",
       "      <td>55.1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.100000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>86.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Together</td>\n",
       "      <td>The economic and financial sanctions already i...</td>\n",
       "      <td>24.7</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>81</td>\n",
       "      <td>87.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>91.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Together</td>\n",
       "      <td>The introduction of a central bank digital cur...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>50.0</td>\n",
       "      <td>9.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>25.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>72</td>\n",
       "      <td>62.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.110000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Together</td>\n",
       "      <td>The introduction of natural experiments to eco...</td>\n",
       "      <td>63.8</td>\n",
       "      <td>32.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>80</td>\n",
       "      <td>97.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.700000e+00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Together</td>\n",
       "      <td>The oil price cap imposed by the G7/EU countri...</td>\n",
       "      <td>5.2</td>\n",
       "      <td>40.3</td>\n",
       "      <td>7.8</td>\n",
       "      <td>1.3</td>\n",
       "      <td>39.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>77</td>\n",
       "      <td>54.5</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000e+00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Together</td>\n",
       "      <td>The ‘credibility revolution’ in empirical econ...</td>\n",
       "      <td>47.5</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80</td>\n",
       "      <td>93.8</td>\n",
       "      <td>...</td>\n",
       "      <td>6.300000e+00</td>\n",
       "      <td>56.0</td>\n",
       "      <td>56.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Together</td>\n",
       "      <td>Artificial intelligence is likely to be a high...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>52.3</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>35.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>65</td>\n",
       "      <td>63.1</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000e+00</td>\n",
       "      <td>99.5</td>\n",
       "      <td>45.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Together</td>\n",
       "      <td>Artificial intelligence offers substantial opp...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>35.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>50.8</td>\n",
       "      <td>3.1</td>\n",
       "      <td>65</td>\n",
       "      <td>46.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.600000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Together</td>\n",
       "      <td>By enabling women’s life choices about educati...</td>\n",
       "      <td>37.7</td>\n",
       "      <td>61.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>98.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>11.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>88.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Together</td>\n",
       "      <td>Even if Argentina could marshal the resources ...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>36.8</td>\n",
       "      <td>10.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33.8</td>\n",
       "      <td>16.2</td>\n",
       "      <td>68</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.030000e+01</td>\n",
       "      <td>53.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Together</td>\n",
       "      <td>Financial regulators in the US and Europe lack...</td>\n",
       "      <td>6.8</td>\n",
       "      <td>30.1</td>\n",
       "      <td>31.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>23.3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>72.6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.700000e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>74.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>27.5</td>\n",
       "      <td>66.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Together</td>\n",
       "      <td>Fiscal rules on budget deficits and public deb...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>40.6</td>\n",
       "      <td>17.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.2</td>\n",
       "      <td>5.8</td>\n",
       "      <td>69</td>\n",
       "      <td>71.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.740000e+01</td>\n",
       "      <td>94.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Together</td>\n",
       "      <td>Fully guaranteeing uninsured deposits at Silic...</td>\n",
       "      <td>13.7</td>\n",
       "      <td>43.8</td>\n",
       "      <td>15.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>21.9</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>74.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.650000e+01</td>\n",
       "      <td>94.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Together</td>\n",
       "      <td>Gender gaps in today’s labor market arise less...</td>\n",
       "      <td>13.0</td>\n",
       "      <td>70.1</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>11.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>88.3</td>\n",
       "      <td>...</td>\n",
       "      <td>5.200000e+00</td>\n",
       "      <td>90.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>58.5</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Together</td>\n",
       "      <td>Given current regulations, non-bank financial ...</td>\n",
       "      <td>7.1</td>\n",
       "      <td>31.4</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.6</td>\n",
       "      <td>8.6</td>\n",
       "      <td>70</td>\n",
       "      <td>52.9</td>\n",
       "      <td>...</td>\n",
       "      <td>1.570000e+01</td>\n",
       "      <td>82.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>15.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>64.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Together</td>\n",
       "      <td>In the absence of continuing flows of Western ...</td>\n",
       "      <td>58.8</td>\n",
       "      <td>36.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>2.5</td>\n",
       "      <td>80</td>\n",
       "      <td>96.2</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>26.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Together</td>\n",
       "      <td>Non-bank financial intermediaries pose a subst...</td>\n",
       "      <td>11.4</td>\n",
       "      <td>65.7</td>\n",
       "      <td>5.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>7.1</td>\n",
       "      <td>70</td>\n",
       "      <td>82.9</td>\n",
       "      <td>...</td>\n",
       "      <td>5.800000e+00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>85.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Together</td>\n",
       "      <td>Not guaranteeing uninsured deposits at Silicon...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>15.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>73</td>\n",
       "      <td>50.7</td>\n",
       "      <td>...</td>\n",
       "      <td>9.600000e+00</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>74.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Together</td>\n",
       "      <td>Regulating the leverage and liquidity of non-b...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>57.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>22.9</td>\n",
       "      <td>7.1</td>\n",
       "      <td>70</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.900000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>69.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Together</td>\n",
       "      <td>Responses To Market Power Constraints on the a...</td>\n",
       "      <td>1.4</td>\n",
       "      <td>27.8</td>\n",
       "      <td>16.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>72</td>\n",
       "      <td>45.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.240000e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>80.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Together</td>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>2.9</td>\n",
       "      <td>46.4</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>15.9</td>\n",
       "      <td>69</td>\n",
       "      <td>56.5</td>\n",
       "      <td>...</td>\n",
       "      <td>7.300000e+00</td>\n",
       "      <td>62.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>86.5</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Together</td>\n",
       "      <td>Since the inception of the Stability and Growt...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>20.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>47.8</td>\n",
       "      <td>17.4</td>\n",
       "      <td>69</td>\n",
       "      <td>34.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.890000e+01</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Together</td>\n",
       "      <td>Subsidizing Green Technology Government subsid...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>59.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>72</td>\n",
       "      <td>87.5</td>\n",
       "      <td>...</td>\n",
       "      <td>2.800000e+00</td>\n",
       "      <td>40.5</td>\n",
       "      <td>68.0</td>\n",
       "      <td>34.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Together</td>\n",
       "      <td>The economic and financial sanctions against R...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>38.8</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>80</td>\n",
       "      <td>65.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.370000e+01</td>\n",
       "      <td>27.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>62.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Together</td>\n",
       "      <td>The effectiveness of existing antitrust regime...</td>\n",
       "      <td>9.7</td>\n",
       "      <td>54.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>29.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>72</td>\n",
       "      <td>68.1</td>\n",
       "      <td>...</td>\n",
       "      <td>4.100000e+00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Together</td>\n",
       "      <td>The fundamental cause of Argentina’s high infl...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>64.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>8.8</td>\n",
       "      <td>68</td>\n",
       "      <td>89.7</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>94.0</td>\n",
       "      <td>22.5</td>\n",
       "      <td>78.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Together</td>\n",
       "      <td>The gender gap in pay would be substantially r...</td>\n",
       "      <td>11.7</td>\n",
       "      <td>59.7</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>72.7</td>\n",
       "      <td>...</td>\n",
       "      <td>1.300000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>63.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Together</td>\n",
       "      <td>The proposed US tariffs on Chinese EVs would l...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40.5</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1.2</td>\n",
       "      <td>46.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>53.6</td>\n",
       "      <td>...</td>\n",
       "      <td>1.200000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>73.5</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Together</td>\n",
       "      <td>The proposed US tariffs on Chinese EVs would m...</td>\n",
       "      <td>10.7</td>\n",
       "      <td>59.5</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>23.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>84</td>\n",
       "      <td>76.2</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000e+00</td>\n",
       "      <td>95.0</td>\n",
       "      <td>13.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.5</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.8</td>\n",
       "      <td>24.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>32.3</td>\n",
       "      <td>9.2</td>\n",
       "      <td>65</td>\n",
       "      <td>58.5</td>\n",
       "      <td>...</td>\n",
       "      <td>1.230000e+01</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>12.3</td>\n",
       "      <td>56.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>18.5</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "      <td>75.4</td>\n",
       "      <td>...</td>\n",
       "      <td>6.100000e+00</td>\n",
       "      <td>99.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Together</td>\n",
       "      <td>The response to recent bank failures should be...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>33.8</td>\n",
       "      <td>33.8</td>\n",
       "      <td>4.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>65</td>\n",
       "      <td>73.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3.850000e+01</td>\n",
       "      <td>6.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>93.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>29.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence is likely to le...</td>\n",
       "      <td>7.7</td>\n",
       "      <td>35.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.6</td>\n",
       "      <td>4.6</td>\n",
       "      <td>65</td>\n",
       "      <td>50.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.230000e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>61.0</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>6.2</td>\n",
       "      <td>30.8</td>\n",
       "      <td>12.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>65</td>\n",
       "      <td>49.2</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000e+00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>48.0</td>\n",
       "      <td>41.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>43.1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>53.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>100.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>27.3</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>88.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>73.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>1.3</td>\n",
       "      <td>44.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>51.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77</td>\n",
       "      <td>48.1</td>\n",
       "      <td>...</td>\n",
       "      <td>2.600000e+00</td>\n",
       "      <td>78.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>21.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>58.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Together</td>\n",
       "      <td>Use of artificial intelligence over the next t...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>46.2</td>\n",
       "      <td>27.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>3.070000e+01</td>\n",
       "      <td>17.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>59.5</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Together</td>\n",
       "      <td>Using subsidies for green technologies instead...</td>\n",
       "      <td>18.1</td>\n",
       "      <td>37.5</td>\n",
       "      <td>15.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>72</td>\n",
       "      <td>70.8</td>\n",
       "      <td>...</td>\n",
       "      <td>1.520000e+01</td>\n",
       "      <td>98.5</td>\n",
       "      <td>23.0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>60 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Euro/US                                              Claim  \\\n",
       "0   Together  A ban on advertising junk foods (those that ar...   \n",
       "1   Together  A federal minimum wage that is pegged to state...   \n",
       "2   Together  A global corporate tax system that is based on...   \n",
       "3   Together  A global minimum corporate tax rate would limi...   \n",
       "4   Together  A mandate for public companies to provide clim...   \n",
       "5   Together  A mandate for public companies to provide clim...   \n",
       "6   Together  A price cap imposed by the G7/EU countries on ...   \n",
       "7   Together  As of now, there needs to be more government r...   \n",
       "8   Together  Central banks that do not introduce their own ...   \n",
       "9   Together  Employees who spend two of their days each wee...   \n",
       "10  Together  Employees who spend two of their days each wee...   \n",
       "11  Together  Having the opportunity to work two to three da...   \n",
       "12  Together  High tariffs imposed by the European Union on ...   \n",
       "13  Together  In pursuit of credible research designs, resea...   \n",
       "14  Together  Network externalities give Twitter an incumben...   \n",
       "15  Together  Policies that aim to reduce obesity by increas...   \n",
       "16  Together  Stablecoins that are not fully backed by eithe...   \n",
       "17  Together  Targeting the Russian economy through a total ...   \n",
       "18  Together  The Bank for International Settlements defines...   \n",
       "19  Together  The UK economy is likely to be at least severa...   \n",
       "20  Together  The aggregate economy of the 27 countries stil...   \n",
       "21  Together  The current US federal minimum wage is $7.25 p...   \n",
       "22  Together  The economic and financial sanctions already i...   \n",
       "23  Together  The introduction of a central bank digital cur...   \n",
       "24  Together  The introduction of natural experiments to eco...   \n",
       "25  Together  The oil price cap imposed by the G7/EU countri...   \n",
       "26  Together  The ‘credibility revolution’ in empirical econ...   \n",
       "27  Together  Artificial intelligence is likely to be a high...   \n",
       "28  Together  Artificial intelligence offers substantial opp...   \n",
       "29  Together  By enabling women’s life choices about educati...   \n",
       "30  Together  Even if Argentina could marshal the resources ...   \n",
       "31  Together  Financial regulators in the US and Europe lack...   \n",
       "32  Together  Fiscal rules on budget deficits and public deb...   \n",
       "33  Together  Fully guaranteeing uninsured deposits at Silic...   \n",
       "34  Together  Gender gaps in today’s labor market arise less...   \n",
       "35  Together  Given current regulations, non-bank financial ...   \n",
       "36  Together  In the absence of continuing flows of Western ...   \n",
       "37  Together  Non-bank financial intermediaries pose a subst...   \n",
       "38  Together  Not guaranteeing uninsured deposits at Silicon...   \n",
       "39  Together  Regulating the leverage and liquidity of non-b...   \n",
       "40  Together  Responses To Market Power Constraints on the a...   \n",
       "41  Together  Since the inception of the Stability and Growt...   \n",
       "42  Together  Since the inception of the Stability and Growt...   \n",
       "43  Together  Subsidizing Green Technology Government subsid...   \n",
       "44  Together  The economic and financial sanctions against R...   \n",
       "45  Together  The effectiveness of existing antitrust regime...   \n",
       "46  Together  The fundamental cause of Argentina’s high infl...   \n",
       "47  Together  The gender gap in pay would be substantially r...   \n",
       "48  Together  The proposed US tariffs on Chinese EVs would l...   \n",
       "49  Together  The proposed US tariffs on Chinese EVs would m...   \n",
       "50  Together  The response to recent bank failures should be...   \n",
       "51  Together  The response to recent bank failures should be...   \n",
       "52  Together  The response to recent bank failures should be...   \n",
       "53  Together  Use of artificial intelligence is likely to le...   \n",
       "54  Together  Use of artificial intelligence over the next t...   \n",
       "55  Together  Use of artificial intelligence over the next t...   \n",
       "56  Together  Use of artificial intelligence over the next t...   \n",
       "57  Together  Use of artificial intelligence over the next t...   \n",
       "58  Together  Use of artificial intelligence over the next t...   \n",
       "59  Together  Using subsidies for green technologies instead...   \n",
       "\n",
       "    Strongly Agree  Agree  Disagree  Strongly Disagree  Uncertain  No Opinion  \\\n",
       "0              3.7   50.0       7.3                0.0       32.9         6.1   \n",
       "1              9.0   43.6      12.8                0.0       32.1         2.6   \n",
       "2              9.3   44.0       1.3                0.0       41.3         4.0   \n",
       "3             22.7   66.7       1.3                0.0        5.3         4.0   \n",
       "4             18.2   63.6       2.6                0.0       14.3         1.3   \n",
       "5              2.6   49.4       2.6                1.3       42.9         1.3   \n",
       "6              6.5   51.9       7.8                1.3       28.6         3.9   \n",
       "7             14.8   42.0       9.9                4.9       27.2         1.2   \n",
       "8              1.4   22.2      31.9                2.8       27.8        13.9   \n",
       "9              2.4   26.8      12.2                0.0       53.7         4.9   \n",
       "10             4.9   57.3       0.0                0.0       32.9         4.9   \n",
       "11             1.2   25.6      14.6                1.2       54.9         2.4   \n",
       "12            11.7   54.5       6.5                0.0       26.0         1.3   \n",
       "13             8.8   42.5       8.8                1.2       33.8         5.0   \n",
       "14            29.6   56.8       6.2                0.0        4.9         2.5   \n",
       "15             2.4   22.0      15.9                2.4       51.2         6.1   \n",
       "16            38.7   50.7       1.3                0.0        2.7         6.7   \n",
       "17            11.1   49.4       2.5                0.0       34.6         2.5   \n",
       "18             9.7   40.3       2.8                0.0       33.3        13.9   \n",
       "19            23.8   57.1       1.2                0.0       15.5         2.4   \n",
       "20             4.8   15.5      32.1                3.6       41.7         2.4   \n",
       "21             2.6   35.9      16.7                0.0       42.3         2.6   \n",
       "22            24.7   63.0       0.0                0.0       11.1         1.2   \n",
       "23             1.4   50.0       9.7                1.4       25.0        12.5   \n",
       "24            63.8   32.5       1.2                0.0        1.2         1.2   \n",
       "25             5.2   40.3       7.8                1.3       39.0         6.5   \n",
       "26            47.5   46.2       0.0                0.0        6.2         0.0   \n",
       "27             3.1   52.3       6.2                1.5       35.4         1.5   \n",
       "28             3.1   35.4       6.2                1.5       50.8         3.1   \n",
       "29            37.7   61.0       0.0                0.0        1.3         0.0   \n",
       "30             2.9   36.8      10.3                0.0       33.8        16.2   \n",
       "31             6.8   30.1      31.5                4.1       23.3         4.1   \n",
       "32            13.0   40.6      17.4                0.0       23.2         5.8   \n",
       "33            13.7   43.8      15.1                1.4       21.9         4.1   \n",
       "34            13.0   70.1       3.9                1.3       11.7         0.0   \n",
       "35             7.1   31.4      14.3                0.0       38.6         8.6   \n",
       "36            58.8   36.2       0.0                1.2        1.2         2.5   \n",
       "37            11.4   65.7       5.7                0.0       10.0         7.1   \n",
       "38             5.5   30.1      15.1                0.0       45.2         4.1   \n",
       "39            10.0   57.1       1.4                1.4       22.9         7.1   \n",
       "40             1.4   27.8      16.7                0.0       43.1        11.1   \n",
       "41             2.9   46.4       7.2                0.0       27.5        15.9   \n",
       "42             0.0   13.0      20.3                1.4       47.8        17.4   \n",
       "43            25.0   59.7       2.8                0.0       11.1         1.4   \n",
       "44             2.5   38.8      23.8                0.0       28.8         6.2   \n",
       "45             9.7   54.2       4.2                0.0       29.2         2.8   \n",
       "46            25.0   64.7       0.0                0.0        1.5         8.8   \n",
       "47            11.7   59.7       1.3                0.0       27.3         0.0   \n",
       "48             0.0   40.5      11.9                1.2       46.4         0.0   \n",
       "49            10.7   59.5       4.8                1.2       23.8         0.0   \n",
       "50             0.0   30.8      24.6                3.1       32.3         9.2   \n",
       "51            12.3   56.9       4.6                1.5       18.5         6.2   \n",
       "52             1.5   33.8      33.8                4.6       20.0         6.2   \n",
       "53             7.7   35.4       7.7                0.0       44.6         4.6   \n",
       "54             6.2   30.8      12.3                0.0       49.2         1.5   \n",
       "55             3.1   43.1       7.7                0.0       46.2         0.0   \n",
       "56             0.0   16.9      10.4                0.0       72.7         0.0   \n",
       "57             1.3   44.2       1.3                1.3       51.9         0.0   \n",
       "58             3.1   46.2      27.7                3.1       20.0         0.0   \n",
       "59            18.1   37.5      15.3                0.0       26.4         2.8   \n",
       "\n",
       "   #Answered  Have An Opinion  ...  Chance More than One Off Prof Choice  \\\n",
       "0         82             61.0  ...                          7.300000e+00   \n",
       "1         78             65.4  ...                          1.270000e+01   \n",
       "2         75             54.7  ...                          1.400000e+00   \n",
       "3         75             90.7  ...                          1.300000e+00   \n",
       "4         77             84.4  ...                          2.600000e+00   \n",
       "5         77             55.8  ...                          3.800000e+00   \n",
       "6         77             67.5  ...                          9.100000e+00   \n",
       "7         81             71.6  ...                          1.480000e+01   \n",
       "8         72             58.3  ...                          2.360000e+01   \n",
       "9         82             41.5  ...                          7.300000e+00   \n",
       "10        82             62.2  ...                          7.105427e-15   \n",
       "11        82             42.7  ...                          4.900000e+00   \n",
       "12        77             72.7  ...                          6.500000e+00   \n",
       "13        80             61.2  ...                          9.900000e+00   \n",
       "14        81             92.6  ...                          6.200000e+00   \n",
       "15        82             42.7  ...                          1.090000e+01   \n",
       "16        75             90.7  ...                          1.200000e+00   \n",
       "17        81             63.0  ...                          2.400000e+00   \n",
       "18        72             52.8  ...                          2.800000e+00   \n",
       "19        84             82.1  ...                          1.200000e+00   \n",
       "20        84             56.0  ...                          1.070000e+01   \n",
       "21        78             55.1  ...                          5.100000e+00   \n",
       "22        81             87.7  ...                          0.000000e+00   \n",
       "23        72             62.5  ...                          1.110000e+01   \n",
       "24        80             97.5  ...                          3.700000e+00   \n",
       "25        77             54.5  ...                          9.000000e+00   \n",
       "26        80             93.8  ...                          6.300000e+00   \n",
       "27        65             63.1  ...                          7.700000e+00   \n",
       "28        65             46.2  ...                          7.600000e+00   \n",
       "29        77             98.7  ...                          0.000000e+00   \n",
       "30        68             50.0  ...                          1.030000e+01   \n",
       "31        73             72.6  ...                          3.700000e+01   \n",
       "32        69             71.0  ...                          1.740000e+01   \n",
       "33        73             74.0  ...                          1.650000e+01   \n",
       "34        77             88.3  ...                          5.200000e+00   \n",
       "35        70             52.9  ...                          1.570000e+01   \n",
       "36        80             96.2  ...                          5.000000e+00   \n",
       "37        70             82.9  ...                          5.800000e+00   \n",
       "38        73             50.7  ...                          9.600000e+00   \n",
       "39        70             70.0  ...                          2.900000e+00   \n",
       "40        72             45.8  ...                          1.240000e+01   \n",
       "41        69             56.5  ...                          7.300000e+00   \n",
       "42        69             34.8  ...                          1.890000e+01   \n",
       "43        72             87.5  ...                          2.800000e+00   \n",
       "44        80             65.0  ...                          2.370000e+01   \n",
       "45        72             68.1  ...                          4.100000e+00   \n",
       "46        68             89.7  ...                          0.000000e+00   \n",
       "47        77             72.7  ...                          1.300000e+00   \n",
       "48        84             53.6  ...                          1.200000e+00   \n",
       "49        84             76.2  ...                          6.000000e+00   \n",
       "50        65             58.5  ...                          1.230000e+01   \n",
       "51        65             75.4  ...                          6.100000e+00   \n",
       "52        65             73.8  ...                          3.850000e+01   \n",
       "53        65             50.8  ...                          1.230000e+01   \n",
       "54        65             49.2  ...                          7.700000e+00   \n",
       "55        65             53.8  ...                          3.000000e+00   \n",
       "56        77             27.3  ...                          0.000000e+00   \n",
       "57        77             48.1  ...                          2.600000e+00   \n",
       "58        65             80.0  ...                          3.070000e+01   \n",
       "59        72             70.8  ...                          1.520000e+01   \n",
       "\n",
       "   Chance GPT4o Top Prof Choice  Chance GPT35 Top Prof Choice  \\\n",
       "0                         100.0                          40.5   \n",
       "1                          96.0                          48.5   \n",
       "2                          25.5                          47.5   \n",
       "3                          98.0                          68.5   \n",
       "4                          53.5                          39.5   \n",
       "5                          90.5                          27.5   \n",
       "6                          45.5                          55.5   \n",
       "7                          98.5                          53.0   \n",
       "8                           0.0                           0.5   \n",
       "9                           0.0                           0.5   \n",
       "10                         99.5                          86.5   \n",
       "11                         84.0                           2.5   \n",
       "12                          0.5                          84.0   \n",
       "13                          7.0                          25.5   \n",
       "14                        100.0                          29.0   \n",
       "15                         59.0                           0.0   \n",
       "16                         87.5                          45.5   \n",
       "17                         94.5                          46.5   \n",
       "18                         95.5                          72.5   \n",
       "19                         96.5                          47.5   \n",
       "20                        100.0                           1.0   \n",
       "21                        100.0                           1.0   \n",
       "22                         91.5                          21.5   \n",
       "23                          0.0                          12.0   \n",
       "24                         22.0                          66.0   \n",
       "25                         26.0                          16.0   \n",
       "26                         56.0                          56.5   \n",
       "27                         99.5                          45.5   \n",
       "28                          0.0                           1.0   \n",
       "29                         11.5                          28.0   \n",
       "30                         53.0                          33.0   \n",
       "31                          1.0                           6.0   \n",
       "32                         94.0                          56.0   \n",
       "33                         94.0                           4.0   \n",
       "34                         90.0                          69.0   \n",
       "35                         82.5                           1.5   \n",
       "36                         26.0                          26.5   \n",
       "37                         38.0                           8.0   \n",
       "38                         17.0                           0.0   \n",
       "39                        100.0                          69.5   \n",
       "40                          1.0                           0.0   \n",
       "41                         62.5                          52.5   \n",
       "42                         99.0                           1.0   \n",
       "43                         40.5                          68.0   \n",
       "44                         27.0                          37.5   \n",
       "45                         80.0                          44.0   \n",
       "46                         94.0                          22.5   \n",
       "47                        100.0                          63.5   \n",
       "48                        100.0                          12.5   \n",
       "49                         95.0                          13.5   \n",
       "50                          1.0                           1.5   \n",
       "51                         99.0                          25.5   \n",
       "52                          6.5                          23.0   \n",
       "53                          0.0                           4.0   \n",
       "54                          0.0                          10.5   \n",
       "55                        100.0                           3.0   \n",
       "56                         88.0                           0.0   \n",
       "57                         78.5                           0.5   \n",
       "58                         17.5                          20.5   \n",
       "59                         98.5                          23.0   \n",
       "\n",
       "    Chance GPT4oProf Top Prof Choice  Chance GPT4o One Off Top Prof Choice  \\\n",
       "0                               59.5                                   0.0   \n",
       "1                               99.0                                   4.0   \n",
       "2                               44.5                                  74.5   \n",
       "3                               98.0                                   2.0   \n",
       "4                               27.5                                  46.5   \n",
       "5                               80.5                                   9.5   \n",
       "6                               69.0                                  54.5   \n",
       "7                               60.5                                   1.5   \n",
       "8                                0.5                                  62.0   \n",
       "9                               11.5                                 100.0   \n",
       "10                              98.5                                   0.5   \n",
       "11                              67.0                                  16.0   \n",
       "12                               0.5                                  99.0   \n",
       "13                               2.0                                   0.5   \n",
       "14                              97.5                                   0.0   \n",
       "15                              65.0                                  41.0   \n",
       "16                              40.5                                  12.5   \n",
       "17                              70.5                                   5.5   \n",
       "18                              74.5                                   4.5   \n",
       "19                             100.0                                   3.5   \n",
       "20                              76.0                                   0.0   \n",
       "21                             100.0                                   0.0   \n",
       "22                              99.5                                   8.5   \n",
       "23                               0.0                                   0.0   \n",
       "24                              91.0                                  78.0   \n",
       "25                               9.0                                  74.0   \n",
       "26                              99.0                                  44.0   \n",
       "27                              96.5                                   0.5   \n",
       "28                               0.0                                  89.5   \n",
       "29                               1.0                                  88.5   \n",
       "30                              78.5                                  47.0   \n",
       "31                              15.5                                  74.0   \n",
       "32                              97.5                                   6.0   \n",
       "33                              91.5                                   6.0   \n",
       "34                              58.5                                  10.0   \n",
       "35                              57.5                                  15.5   \n",
       "36                              78.0                                  74.0   \n",
       "37                              75.5                                  62.0   \n",
       "38                              19.5                                  74.5   \n",
       "39                              99.5                                   0.0   \n",
       "40                               7.5                                  98.5   \n",
       "41                              86.5                                  37.5   \n",
       "42                              99.0                                   1.0   \n",
       "43                              34.5                                  59.5   \n",
       "44                              32.0                                  73.0   \n",
       "45                              83.5                                  20.0   \n",
       "46                              78.0                                   6.0   \n",
       "47                              90.0                                   0.0   \n",
       "48                              95.5                                   0.0   \n",
       "49                              95.0                                   5.0   \n",
       "50                               0.0                                  99.0   \n",
       "51                              99.0                                   1.0   \n",
       "52                               2.5                                  93.5   \n",
       "53                               0.0                                  99.5   \n",
       "54                               0.0                                 100.0   \n",
       "55                              97.0                                   0.0   \n",
       "56                              72.0                                  12.0   \n",
       "57                              95.0                                  21.5   \n",
       "58                              73.0                                  81.0   \n",
       "59                              96.5                                   1.5   \n",
       "\n",
       "    Chance GPT4o More than One Off Top Prof Choice  \\\n",
       "0                                              0.0   \n",
       "1                                              0.0   \n",
       "2                                              0.0   \n",
       "3                                              0.0   \n",
       "4                                              0.0   \n",
       "5                                              0.0   \n",
       "6                                              0.0   \n",
       "7                                              0.0   \n",
       "8                                             38.0   \n",
       "9                                              0.0   \n",
       "10                                             0.0   \n",
       "11                                             0.0   \n",
       "12                                             0.5   \n",
       "13                                            92.5   \n",
       "14                                             0.0   \n",
       "15                                             0.0   \n",
       "16                                             0.0   \n",
       "17                                             0.0   \n",
       "18                                             0.0   \n",
       "19                                             0.0   \n",
       "20                                             0.0   \n",
       "21                                             0.0   \n",
       "22                                             0.0   \n",
       "23                                           100.0   \n",
       "24                                             0.0   \n",
       "25                                             0.0   \n",
       "26                                             0.0   \n",
       "27                                             0.0   \n",
       "28                                            10.5   \n",
       "29                                             0.0   \n",
       "30                                             0.0   \n",
       "31                                            25.0   \n",
       "32                                             0.0   \n",
       "33                                             0.0   \n",
       "34                                             0.0   \n",
       "35                                             2.0   \n",
       "36                                             0.0   \n",
       "37                                             0.0   \n",
       "38                                             8.5   \n",
       "39                                             0.0   \n",
       "40                                             0.5   \n",
       "41                                             0.0   \n",
       "42                                             0.0   \n",
       "43                                             0.0   \n",
       "44                                             0.0   \n",
       "45                                             0.0   \n",
       "46                                             0.0   \n",
       "47                                             0.0   \n",
       "48                                             0.0   \n",
       "49                                             0.0   \n",
       "50                                             0.0   \n",
       "51                                             0.0   \n",
       "52                                             0.0   \n",
       "53                                             0.5   \n",
       "54                                             0.0   \n",
       "55                                             0.0   \n",
       "56                                             0.0   \n",
       "57                                             0.0   \n",
       "58                                             1.5   \n",
       "59                                             0.0   \n",
       "\n",
       "    Chance GPT4oProf One Off Top Prof Choice  \\\n",
       "0                                       40.5   \n",
       "1                                        1.0   \n",
       "2                                       55.5   \n",
       "3                                        2.0   \n",
       "4                                       72.5   \n",
       "5                                       19.5   \n",
       "6                                       31.0   \n",
       "7                                       39.5   \n",
       "8                                       81.5   \n",
       "9                                       88.5   \n",
       "10                                       1.5   \n",
       "11                                      32.5   \n",
       "12                                      90.0   \n",
       "13                                       0.5   \n",
       "14                                       2.5   \n",
       "15                                      35.0   \n",
       "16                                      59.5   \n",
       "17                                      29.5   \n",
       "18                                      25.5   \n",
       "19                                       0.0   \n",
       "20                                      23.5   \n",
       "21                                       0.0   \n",
       "22                                       0.5   \n",
       "23                                       0.5   \n",
       "24                                       9.0   \n",
       "25                                      86.5   \n",
       "26                                       1.0   \n",
       "27                                       3.5   \n",
       "28                                      97.0   \n",
       "29                                      99.0   \n",
       "30                                      19.0   \n",
       "31                                      70.0   \n",
       "32                                       2.5   \n",
       "33                                       8.5   \n",
       "34                                      41.5   \n",
       "35                                      42.5   \n",
       "36                                      22.0   \n",
       "37                                      24.5   \n",
       "38                                      78.0   \n",
       "39                                       0.5   \n",
       "40                                      92.0   \n",
       "41                                      13.5   \n",
       "42                                       1.0   \n",
       "43                                      65.5   \n",
       "44                                      68.0   \n",
       "45                                      16.5   \n",
       "46                                      22.0   \n",
       "47                                      10.0   \n",
       "48                                       4.5   \n",
       "49                                       5.0   \n",
       "50                                     100.0   \n",
       "51                                       1.0   \n",
       "52                                      85.0   \n",
       "53                                      98.5   \n",
       "54                                      97.5   \n",
       "55                                       3.0   \n",
       "56                                      28.0   \n",
       "57                                       5.0   \n",
       "58                                      25.0   \n",
       "59                                       3.5   \n",
       "\n",
       "    Chance GPT4oProf More than One Off Top Prof Choice  \\\n",
       "0                                                 0.0    \n",
       "1                                                 0.0    \n",
       "2                                                 0.0    \n",
       "3                                                 0.0    \n",
       "4                                                 0.0    \n",
       "5                                                 0.0    \n",
       "6                                                 0.0    \n",
       "7                                                 0.0    \n",
       "8                                                18.0    \n",
       "9                                                 0.0    \n",
       "10                                                0.0    \n",
       "11                                                0.5    \n",
       "12                                                9.5    \n",
       "13                                               97.5    \n",
       "14                                                0.0    \n",
       "15                                                0.0    \n",
       "16                                                0.0    \n",
       "17                                                0.0    \n",
       "18                                                0.0    \n",
       "19                                                0.0    \n",
       "20                                                0.5    \n",
       "21                                                0.0    \n",
       "22                                                0.0    \n",
       "23                                               99.5    \n",
       "24                                                0.0    \n",
       "25                                                4.5    \n",
       "26                                                0.0    \n",
       "27                                                0.0    \n",
       "28                                                3.0    \n",
       "29                                                0.0    \n",
       "30                                                2.5    \n",
       "31                                               14.5    \n",
       "32                                                0.0    \n",
       "33                                                0.0    \n",
       "34                                                0.0    \n",
       "35                                                0.0    \n",
       "36                                                0.0    \n",
       "37                                                0.0    \n",
       "38                                                2.5    \n",
       "39                                                0.0    \n",
       "40                                                0.5    \n",
       "41                                                0.0    \n",
       "42                                                0.0    \n",
       "43                                                0.0    \n",
       "44                                                0.0    \n",
       "45                                                0.0    \n",
       "46                                                0.0    \n",
       "47                                                0.0    \n",
       "48                                                0.0    \n",
       "49                                                0.0    \n",
       "50                                                0.0    \n",
       "51                                                0.0    \n",
       "52                                               12.5    \n",
       "53                                                1.5    \n",
       "54                                                2.5    \n",
       "55                                                0.0    \n",
       "56                                                0.0    \n",
       "57                                                0.0    \n",
       "58                                                2.0    \n",
       "59                                                0.0    \n",
       "\n",
       "    Chance GPT35 One Off Top Prof Choice  \\\n",
       "0                                   59.5   \n",
       "1                                   49.5   \n",
       "2                                   50.0   \n",
       "3                                   31.0   \n",
       "4                                   60.5   \n",
       "5                                   72.5   \n",
       "6                                   43.5   \n",
       "7                                   47.0   \n",
       "8                                    5.5   \n",
       "9                                   69.5   \n",
       "10                                  13.5   \n",
       "11                                  52.0   \n",
       "12                                  14.5   \n",
       "13                                   4.0   \n",
       "14                                  46.5   \n",
       "15                                  56.5   \n",
       "16                                  32.5   \n",
       "17                                  23.0   \n",
       "18                                  27.5   \n",
       "19                                  37.0   \n",
       "20                                  62.0   \n",
       "21                                  12.5   \n",
       "22                                  15.0   \n",
       "23                                   5.0   \n",
       "24                                  34.0   \n",
       "25                                   4.5   \n",
       "26                                  43.5   \n",
       "27                                  42.5   \n",
       "28                                  55.5   \n",
       "29                                  72.0   \n",
       "30                                  12.0   \n",
       "31                                  27.5   \n",
       "32                                  44.0   \n",
       "33                                   0.5   \n",
       "34                                  31.0   \n",
       "35                                  34.5   \n",
       "36                                  28.5   \n",
       "37                                   7.0   \n",
       "38                                   8.5   \n",
       "39                                  30.5   \n",
       "40                                  80.0   \n",
       "41                                  47.5   \n",
       "42                                  69.0   \n",
       "43                                  32.0   \n",
       "44                                  62.5   \n",
       "45                                  56.0   \n",
       "46                                  76.0   \n",
       "47                                  36.5   \n",
       "48                                  73.5   \n",
       "49                                  28.5   \n",
       "50                                  61.0   \n",
       "51                                  74.5   \n",
       "52                                  29.0   \n",
       "53                                  61.0   \n",
       "54                                  48.0   \n",
       "55                                  43.0   \n",
       "56                                  26.5   \n",
       "57                                  41.5   \n",
       "58                                  59.5   \n",
       "59                                   8.0   \n",
       "\n",
       "    Chance GPT35 More than One Off Top Prof Choice  \n",
       "0                                              0.0  \n",
       "1                                              2.0  \n",
       "2                                              2.5  \n",
       "3                                              0.5  \n",
       "4                                              0.0  \n",
       "5                                              0.0  \n",
       "6                                              1.0  \n",
       "7                                              0.0  \n",
       "8                                             94.0  \n",
       "9                                             30.0  \n",
       "10                                             0.0  \n",
       "11                                            45.5  \n",
       "12                                             1.5  \n",
       "13                                            70.5  \n",
       "14                                            24.5  \n",
       "15                                            43.5  \n",
       "16                                            22.0  \n",
       "17                                            30.5  \n",
       "18                                             0.0  \n",
       "19                                            15.5  \n",
       "20                                            37.0  \n",
       "21                                            86.5  \n",
       "22                                            63.5  \n",
       "23                                            83.0  \n",
       "24                                             0.0  \n",
       "25                                            79.5  \n",
       "26                                             0.0  \n",
       "27                                            12.0  \n",
       "28                                            43.5  \n",
       "29                                             0.0  \n",
       "30                                            55.0  \n",
       "31                                            66.5  \n",
       "32                                             0.0  \n",
       "33                                            95.5  \n",
       "34                                             0.0  \n",
       "35                                            64.0  \n",
       "36                                            45.0  \n",
       "37                                            85.0  \n",
       "38                                            91.5  \n",
       "39                                             0.0  \n",
       "40                                            20.0  \n",
       "41                                             0.0  \n",
       "42                                            30.0  \n",
       "43                                             0.0  \n",
       "44                                             0.0  \n",
       "45                                             0.0  \n",
       "46                                             1.5  \n",
       "47                                             0.0  \n",
       "48                                            14.0  \n",
       "49                                            58.0  \n",
       "50                                            37.5  \n",
       "51                                             0.0  \n",
       "52                                            48.0  \n",
       "53                                            35.0  \n",
       "54                                            41.5  \n",
       "55                                            54.0  \n",
       "56                                            73.5  \n",
       "57                                            58.0  \n",
       "58                                            20.0  \n",
       "59                                            69.0  \n",
       "\n",
       "[60 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# computing stats for ChatGPT data\n",
    "GPT4overview=pd.DataFrame(0,index=np.arange(0), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "\n",
    "for i in togetheropinion['Claim']:\n",
    "    b=df[df['claimtext']==i]\n",
    "    c=b['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
    "    a=pd.DataFrame(0,index=np.arange(1), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "    a.at[0,'Claim']=i\n",
    "    for j in c.index:\n",
    "        a.at[0,j+' ChatGPT']=c.loc[j]\n",
    "    \n",
    "    GPT4overview=pd.concat([GPT4overview, a], axis=0)\n",
    "GPT4overview=GPT4overview.reset_index(drop=True)\n",
    "GPT4overview.columns = map(str.title, GPT4overview.columns)\n",
    "display(GPT4overview)\n",
    "\n",
    "GPT4Profoverview=pd.DataFrame(0,index=np.arange(0), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "\n",
    "for i in togetheropinion['Claim']:\n",
    "    b=df[df['claimtext']==i]\n",
    "    c=b['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
    "    a=pd.DataFrame(0,index=np.arange(1), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "    a.at[0,'Claim']=i\n",
    "    for j in c.index:\n",
    "        a.at[0,j+' ChatGPT']=c.loc[j]\n",
    "    \n",
    "    GPT4Profoverview=pd.concat([GPT4Profoverview, a], axis=0)\n",
    "GPT4Profoverview=GPT4Profoverview.reset_index(drop=True)\n",
    "GPT4Profoverview.columns = map(str.title, GPT4Profoverview.columns)\n",
    "display(GPT4Profoverview)\n",
    "\n",
    "GPT35overview=pd.DataFrame(0,index=np.arange(0), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "\n",
    "for i in togetheropinion['Claim']:\n",
    "    b=df[df['claimtext']==i]\n",
    "    c=b['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
    "    a=pd.DataFrame(0,index=np.arange(1), columns=['Claim','strongly agree ChatGPT', 'agree ChatGPT', 'disagree ChatGPT', 'strongly disagree ChatGPT','uncertain ChatGPT', 'no opinion ChatGPT'])\n",
    "    a.at[0,'Claim']=i\n",
    "    for j in c.index:\n",
    "        a.at[0,j+' ChatGPT']=c.loc[j]\n",
    "    \n",
    "    GPT35overview=pd.concat([GPT35overview, a], axis=0)\n",
    "GPT35overview=GPT35overview.reset_index(drop=True)\n",
    "GPT35overview.columns = map(str.title, GPT35overview.columns)\n",
    "display(GPT35overview)\n",
    "\n",
    "# adding chance of getting the consensus when one asks chatGPT\n",
    "for j in range(0, len(togetheropinion)):\n",
    "\n",
    "    togetheropinion.at[j,'Chance GPT4o Top Prof Choice']=GPT4overview[togetheropinion['Median'][j]+' Chatgpt'][j]\n",
    "    togetheropinion.at[j,'Chance GPT35 Top Prof Choice']=GPT35overview[togetheropinion['Median'][j]+' Chatgpt'][j]\n",
    "    togetheropinion.at[j,'Chance GPT4oProf Top Prof Choice']=GPT4Profoverview[togetheropinion['Median'][j]+' Chatgpt'][j]\n",
    "        \n",
    "    b=a1.loc[0]==a1[togetheropinion['Median'][j]].to_list()[0]+1\n",
    "    \n",
    "    try:\n",
    "        b1=b[b==True].idxmax()\n",
    "    except:\n",
    "        b1='to be removed'\n",
    "    \n",
    "    b=a1.loc[0]==a1[togetheropinion['Median'][j]].to_list()[0]-1\n",
    "    \n",
    "    try:\n",
    "        b2=b[b==True].idxmax()\n",
    "    except:\n",
    "        b2='to be removed'\n",
    "    b=[b1,b2]\n",
    "    c=[]\n",
    "    \n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=[i+' Chatgpt']\n",
    "    \n",
    "    for i in b:\n",
    "        if 'Uncertain' in i:\n",
    "            c+=['No Opinion' +' Chatgpt']\n",
    "    for i in b:\n",
    "        if 'No Opinion' in i:\n",
    "            c+=['Uncertain' +' Chatgpt']\n",
    "    togetheropinion.at[j,'Chance GPT4o One Off Top Prof Choice']=np.sum(GPT4overview[c].loc[j])\n",
    "    togetheropinion.at[j,'Chance GPT4o More than One Off Top Prof Choice']=100-togetheropinion.at[j,'Chance GPT4o One Off Top Prof Choice']-togetheropinion.at[j,'Chance GPT4o Top Prof Choice']\n",
    "\n",
    "    togetheropinion.at[j,'Chance GPT4oProf One Off Top Prof Choice']=np.sum(GPT4Profoverview[c].loc[j])\n",
    "    togetheropinion.at[j,'Chance GPT4oProf More than One Off Top Prof Choice']=100-togetheropinion.at[j,'Chance GPT4oProf One Off Top Prof Choice']-togetheropinion.at[j,'Chance GPT4oProf Top Prof Choice']\n",
    "\n",
    "    togetheropinion.at[j,'Chance GPT35 One Off Top Prof Choice']=np.sum(GPT35overview[c].loc[j])\n",
    "    togetheropinion.at[j,'Chance GPT35 More than One Off Top Prof Choice']=100-togetheropinion.at[j,'Chance GPT35 One Off Top Prof Choice']-togetheropinion.at[j,'Chance GPT35 Top Prof Choice']\n",
    "\n",
    "togetheropinion\n",
    "    \n",
    "    #    togetheropinion.at[0,'Change GPT One Off Prof Choice']=GPT4overview['Uncertain'+' Chatgpt'][0]+GPT4overview['Uncertain'+' Chatgpt'][0]\n",
    "    #else:\n",
    "    #    togetheropinion.at[0,'Change GPT One Off Prof Choice']=GPT4overview[b.idxmax()+' Chatgpt'][0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1ae38261-5ab8-4fc9-adc6-0552b58d39ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Strongly Agree</th>\n",
       "      <th>Agree</th>\n",
       "      <th>Uncertain</th>\n",
       "      <th>Disagree</th>\n",
       "      <th>Strongly Disagree</th>\n",
       "      <th>No Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.846667</td>\n",
       "      <td>43.838333</td>\n",
       "      <td>29.166667</td>\n",
       "      <td>9.708333</td>\n",
       "      <td>0.853333</td>\n",
       "      <td>4.593333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.825000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>19.625000</td>\n",
       "      <td>2.575000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>43.700000</td>\n",
       "      <td>28.700000</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.175000</td>\n",
       "      <td>55.075000</td>\n",
       "      <td>41.850000</td>\n",
       "      <td>14.725000</td>\n",
       "      <td>1.325000</td>\n",
       "      <td>6.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>63.800000</td>\n",
       "      <td>70.100000</td>\n",
       "      <td>72.700000</td>\n",
       "      <td>33.800000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Strongly Agree      Agree  Uncertain   Disagree  Strongly Disagree  \\\n",
       "mean       11.846667  43.838333  29.166667   9.708333           0.853333   \n",
       "min         0.000000  13.000000   1.200000   0.000000           0.000000   \n",
       "25%         2.825000  35.000000  19.625000   2.575000           0.000000   \n",
       "50%         7.400000  43.700000  28.700000   7.500000           0.000000   \n",
       "75%        13.175000  55.075000  41.850000  14.725000           1.325000   \n",
       "max        63.800000  70.100000  72.700000  33.800000           4.900000   \n",
       "\n",
       "      No Opinion  \n",
       "mean    4.593333  \n",
       "min     0.000000  \n",
       "25%     1.300000  \n",
       "50%     3.500000  \n",
       "75%     6.200000  \n",
       "max    17.400000  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=togetheropinion[['Strongly Agree','Agree','Uncertain', 'Disagree','Strongly Disagree','No Opinion']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "127a9c78-46b6-418e-b379-a4641a3bcceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "#T_b699b td {\n",
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       "</style>\n",
       "<table id=\"T_b699b\">\n",
       "  <caption>Table 1: Distribution of Answers - Economics Prof Survey vs. ChatGPT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b699b_level0_col0\" class=\"col_heading level0 col0\" colspan=\"6\">Economics Profs Survey</th>\n",
       "      <th id=\"T_b699b_level0_col6\" class=\"col_heading level0 col6\" colspan=\"6\">ChatGPT 3.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_b699b_level1_col0\" class=\"col_heading level1 col0\" >Strongly Agree</th>\n",
       "      <th id=\"T_b699b_level1_col1\" class=\"col_heading level1 col1\" >Agree</th>\n",
       "      <th id=\"T_b699b_level1_col2\" class=\"col_heading level1 col2\" >Uncertain</th>\n",
       "      <th id=\"T_b699b_level1_col3\" class=\"col_heading level1 col3\" >Disagree</th>\n",
       "      <th id=\"T_b699b_level1_col4\" class=\"col_heading level1 col4\" >Strongly Disagree</th>\n",
       "      <th id=\"T_b699b_level1_col5\" class=\"col_heading level1 col5\" >No Opinion</th>\n",
       "      <th id=\"T_b699b_level1_col6\" class=\"col_heading level1 col6\" >Strongly Agree</th>\n",
       "      <th id=\"T_b699b_level1_col7\" class=\"col_heading level1 col7\" >Agree</th>\n",
       "      <th id=\"T_b699b_level1_col8\" class=\"col_heading level1 col8\" >Uncertain</th>\n",
       "      <th id=\"T_b699b_level1_col9\" class=\"col_heading level1 col9\" >Disagree</th>\n",
       "      <th id=\"T_b699b_level1_col10\" class=\"col_heading level1 col10\" >Strongly Disagree</th>\n",
       "      <th id=\"T_b699b_level1_col11\" class=\"col_heading level1 col11\" >No Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row0\" class=\"row_heading level0 row0\" >mean</th>\n",
       "      <td id=\"T_b699b_row0_col0\" class=\"data row0 col0 false \" >11.8</td>\n",
       "      <td id=\"T_b699b_row0_col1\" class=\"data row0 col1 false \" >43.8</td>\n",
       "      <td id=\"T_b699b_row0_col2\" class=\"data row0 col2 false \" >29.2</td>\n",
       "      <td id=\"T_b699b_row0_col3\" class=\"data row0 col3 false \" >9.7</td>\n",
       "      <td id=\"T_b699b_row0_col4\" class=\"data row0 col4 false \" >0.9</td>\n",
       "      <td id=\"T_b699b_row0_col5\" class=\"data row0 col5 true \" >4.6</td>\n",
       "      <td id=\"T_b699b_row0_col6\" class=\"data row0 col6\" >33.6</td>\n",
       "      <td id=\"T_b699b_row0_col7\" class=\"data row0 col7\" >42.3</td>\n",
       "      <td id=\"T_b699b_row0_col8\" class=\"data row0 col8\" >2.3</td>\n",
       "      <td id=\"T_b699b_row0_col9\" class=\"data row0 col9\" >5.8</td>\n",
       "      <td id=\"T_b699b_row0_col10\" class=\"data row0 col10\" >15.9</td>\n",
       "      <td id=\"T_b699b_row0_col11\" class=\"data row0 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row1\" class=\"row_heading level0 row1\" >min</th>\n",
       "      <td id=\"T_b699b_row1_col0\" class=\"data row1 col0 false\" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col1\" class=\"data row1 col1 false \" >13.0</td>\n",
       "      <td id=\"T_b699b_row1_col2\" class=\"data row1 col2 false \" >1.2</td>\n",
       "      <td id=\"T_b699b_row1_col3\" class=\"data row1 col3 false \" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col4\" class=\"data row1 col4 false \" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col5\" class=\"data row1 col5 true \" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col6\" class=\"data row1 col6\" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col7\" class=\"data row1 col7\" >2.0</td>\n",
       "      <td id=\"T_b699b_row1_col8\" class=\"data row1 col8\" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col9\" class=\"data row1 col9\" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col10\" class=\"data row1 col10\" >0.0</td>\n",
       "      <td id=\"T_b699b_row1_col11\" class=\"data row1 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row2\" class=\"row_heading level0 row2\" >25%</th>\n",
       "      <td id=\"T_b699b_row2_col0\" class=\"data row2 col0 false \" >2.8</td>\n",
       "      <td id=\"T_b699b_row2_col1\" class=\"data row2 col1 false \" >35.0</td>\n",
       "      <td id=\"T_b699b_row2_col2\" class=\"data row2 col2 false \" >19.6</td>\n",
       "      <td id=\"T_b699b_row2_col3\" class=\"data row2 col3 false \" >2.6</td>\n",
       "      <td id=\"T_b699b_row2_col4\" class=\"data row2 col4 false \" >0.0</td>\n",
       "      <td id=\"T_b699b_row2_col5\" class=\"data row2 col5 true \" >1.3</td>\n",
       "      <td id=\"T_b699b_row2_col6\" class=\"data row2 col6\" >13.0</td>\n",
       "      <td id=\"T_b699b_row2_col7\" class=\"data row2 col7\" >25.5</td>\n",
       "      <td id=\"T_b699b_row2_col8\" class=\"data row2 col8\" >0.0</td>\n",
       "      <td id=\"T_b699b_row2_col9\" class=\"data row2 col9\" >0.0</td>\n",
       "      <td id=\"T_b699b_row2_col10\" class=\"data row2 col10\" >0.0</td>\n",
       "      <td id=\"T_b699b_row2_col11\" class=\"data row2 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row3\" class=\"row_heading level0 row3\" >50%</th>\n",
       "      <td id=\"T_b699b_row3_col0\" class=\"data row3 col0 false \" >7.4</td>\n",
       "      <td id=\"T_b699b_row3_col1\" class=\"data row3 col1 false \" >43.7</td>\n",
       "      <td id=\"T_b699b_row3_col2\" class=\"data row3 col2 false \" >28.7</td>\n",
       "      <td id=\"T_b699b_row3_col3\" class=\"data row3 col3 false \" >7.5</td>\n",
       "      <td id=\"T_b699b_row3_col4\" class=\"data row3 col4 false \" >0.0</td>\n",
       "      <td id=\"T_b699b_row3_col5\" class=\"data row3 col5 true \" >3.5</td>\n",
       "      <td id=\"T_b699b_row3_col6\" class=\"data row3 col6\" >31.0</td>\n",
       "      <td id=\"T_b699b_row3_col7\" class=\"data row3 col7\" >43.8</td>\n",
       "      <td id=\"T_b699b_row3_col8\" class=\"data row3 col8\" >0.5</td>\n",
       "      <td id=\"T_b699b_row3_col9\" class=\"data row3 col9\" >0.5</td>\n",
       "      <td id=\"T_b699b_row3_col10\" class=\"data row3 col10\" >1.0</td>\n",
       "      <td id=\"T_b699b_row3_col11\" class=\"data row3 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row4\" class=\"row_heading level0 row4\" >75%</th>\n",
       "      <td id=\"T_b699b_row4_col0\" class=\"data row4 col0 false \" >13.2</td>\n",
       "      <td id=\"T_b699b_row4_col1\" class=\"data row4 col1 false \" >55.1</td>\n",
       "      <td id=\"T_b699b_row4_col2\" class=\"data row4 col2 false \" >41.9</td>\n",
       "      <td id=\"T_b699b_row4_col3\" class=\"data row4 col3 false \" >14.7</td>\n",
       "      <td id=\"T_b699b_row4_col4\" class=\"data row4 col4 false \" >1.3</td>\n",
       "      <td id=\"T_b699b_row4_col5\" class=\"data row4 col5 true \" >6.2</td>\n",
       "      <td id=\"T_b699b_row4_col6\" class=\"data row4 col6\" >47.8</td>\n",
       "      <td id=\"T_b699b_row4_col7\" class=\"data row4 col7\" >56.5</td>\n",
       "      <td id=\"T_b699b_row4_col8\" class=\"data row4 col8\" >2.0</td>\n",
       "      <td id=\"T_b699b_row4_col9\" class=\"data row4 col9\" >6.5</td>\n",
       "      <td id=\"T_b699b_row4_col10\" class=\"data row4 col10\" >26.5</td>\n",
       "      <td id=\"T_b699b_row4_col11\" class=\"data row4 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b699b_level0_row5\" class=\"row_heading level0 row5\" >max</th>\n",
       "      <td id=\"T_b699b_row5_col0\" class=\"data row5 col0 false \" >63.8</td>\n",
       "      <td id=\"T_b699b_row5_col1\" class=\"data row5 col1 false \" >70.1</td>\n",
       "      <td id=\"T_b699b_row5_col2\" class=\"data row5 col2 false \" >72.7</td>\n",
       "      <td id=\"T_b699b_row5_col3\" class=\"data row5 col3 false \" >33.8</td>\n",
       "      <td id=\"T_b699b_row5_col4\" class=\"data row5 col4 false \" >4.9</td>\n",
       "      <td id=\"T_b699b_row5_col5\" class=\"data row5 col5 true \" >17.4</td>\n",
       "      <td id=\"T_b699b_row5_col6\" class=\"data row5 col6\" >82.0</td>\n",
       "      <td id=\"T_b699b_row5_col7\" class=\"data row5 col7\" >86.5</td>\n",
       "      <td id=\"T_b699b_row5_col8\" class=\"data row5 col8\" >52.5</td>\n",
       "      <td id=\"T_b699b_row5_col9\" class=\"data row5 col9\" >60.5</td>\n",
       "      <td id=\"T_b699b_row5_col10\" class=\"data row5 col10\" >86.5</td>\n",
       "      <td id=\"T_b699b_row5_col11\" class=\"data row5 col11\" >0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_82515_level0_col0\" class=\"col_heading level0 col0\" colspan=\"6\">ChatGPT 4o</th>\n",
       "      <th id=\"T_82515_level0_col6\" class=\"col_heading level0 col6\" colspan=\"6\">ChatGPT 4oProf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_82515_level1_col0\" class=\"col_heading level1 col0\" >Strongly Agree</th>\n",
       "      <th id=\"T_82515_level1_col1\" class=\"col_heading level1 col1\" >Agree</th>\n",
       "      <th id=\"T_82515_level1_col2\" class=\"col_heading level1 col2\" >Uncertain</th>\n",
       "      <th id=\"T_82515_level1_col3\" class=\"col_heading level1 col3\" >Disagree</th>\n",
       "      <th id=\"T_82515_level1_col4\" class=\"col_heading level1 col4\" >Strongly Disagree</th>\n",
       "      <th id=\"T_82515_level1_col5\" class=\"col_heading level1 col5\" >No Opinion</th>\n",
       "      <th id=\"T_82515_level1_col6\" class=\"col_heading level1 col6\" >Strongly Agree</th>\n",
       "      <th id=\"T_82515_level1_col7\" class=\"col_heading level1 col7\" >Agree</th>\n",
       "      <th id=\"T_82515_level1_col8\" class=\"col_heading level1 col8\" >Uncertain</th>\n",
       "      <th id=\"T_82515_level1_col9\" class=\"col_heading level1 col9\" >Disagree</th>\n",
       "      <th id=\"T_82515_level1_col10\" class=\"col_heading level1 col10\" >Strongly Disagree</th>\n",
       "      <th id=\"T_82515_level1_col11\" class=\"col_heading level1 col11\" >No Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row0\" class=\"row_heading level0 row0\" >mean</th>\n",
       "      <td id=\"T_82515_row0_col0\" class=\"data row0 col0 false \" >6.7</td>\n",
       "      <td id=\"T_82515_row0_col1\" class=\"data row0 col1 false \" >60.4</td>\n",
       "      <td id=\"T_82515_row0_col2\" class=\"data row0 col2 false \" >29.5</td>\n",
       "      <td id=\"T_82515_row0_col3\" class=\"data row0 col3 false \" >3.2</td>\n",
       "      <td id=\"T_82515_row0_col4\" class=\"data row0 col4 false \" >0.2</td>\n",
       "      <td id=\"T_82515_row0_col5\" class=\"data row0 col5 true \" >0.0</td>\n",
       "      <td id=\"T_82515_row0_col6\" class=\"data row0 col6\" >12.2</td>\n",
       "      <td id=\"T_82515_row0_col7\" class=\"data row0 col7\" >55.7</td>\n",
       "      <td id=\"T_82515_row0_col8\" class=\"data row0 col8\" >27.2</td>\n",
       "      <td id=\"T_82515_row0_col9\" class=\"data row0 col9\" >4.6</td>\n",
       "      <td id=\"T_82515_row0_col10\" class=\"data row0 col10\" >0.3</td>\n",
       "      <td id=\"T_82515_row0_col11\" class=\"data row0 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row1\" class=\"row_heading level0 row1\" >min</th>\n",
       "      <td id=\"T_82515_row1_col0\" class=\"data row1 col0 false\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col1\" class=\"data row1 col1 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col2\" class=\"data row1 col2 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col3\" class=\"data row1 col3 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col4\" class=\"data row1 col4 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col5\" class=\"data row1 col5 true \" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col6\" class=\"data row1 col6\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col7\" class=\"data row1 col7\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col8\" class=\"data row1 col8\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col9\" class=\"data row1 col9\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col10\" class=\"data row1 col10\" >0.0</td>\n",
       "      <td id=\"T_82515_row1_col11\" class=\"data row1 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row2\" class=\"row_heading level0 row2\" >25%</th>\n",
       "      <td id=\"T_82515_row2_col0\" class=\"data row2 col0 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col1\" class=\"data row2 col1 false \" >22.2</td>\n",
       "      <td id=\"T_82515_row2_col2\" class=\"data row2 col2 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col3\" class=\"data row2 col3 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col4\" class=\"data row2 col4 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col5\" class=\"data row2 col5 true \" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col6\" class=\"data row2 col6\" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col7\" class=\"data row2 col7\" >17.1</td>\n",
       "      <td id=\"T_82515_row2_col8\" class=\"data row2 col8\" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col9\" class=\"data row2 col9\" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col10\" class=\"data row2 col10\" >0.0</td>\n",
       "      <td id=\"T_82515_row2_col11\" class=\"data row2 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row3\" class=\"row_heading level0 row3\" >50%</th>\n",
       "      <td id=\"T_82515_row3_col0\" class=\"data row3 col0 false \" >0.2</td>\n",
       "      <td id=\"T_82515_row3_col1\" class=\"data row3 col1 false \" >76.2</td>\n",
       "      <td id=\"T_82515_row3_col2\" class=\"data row3 col2 false \" >1.2</td>\n",
       "      <td id=\"T_82515_row3_col3\" class=\"data row3 col3 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row3_col4\" class=\"data row3 col4 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row3_col5\" class=\"data row3 col5 true \" >0.0</td>\n",
       "      <td id=\"T_82515_row3_col6\" class=\"data row3 col6\" >0.5</td>\n",
       "      <td id=\"T_82515_row3_col7\" class=\"data row3 col7\" >69.8</td>\n",
       "      <td id=\"T_82515_row3_col8\" class=\"data row3 col8\" >3.0</td>\n",
       "      <td id=\"T_82515_row3_col9\" class=\"data row3 col9\" >0.0</td>\n",
       "      <td id=\"T_82515_row3_col10\" class=\"data row3 col10\" >0.0</td>\n",
       "      <td id=\"T_82515_row3_col11\" class=\"data row3 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row4\" class=\"row_heading level0 row4\" >75%</th>\n",
       "      <td id=\"T_82515_row4_col0\" class=\"data row4 col0 false \" >3.2</td>\n",
       "      <td id=\"T_82515_row4_col1\" class=\"data row4 col1 false \" >96.9</td>\n",
       "      <td id=\"T_82515_row4_col2\" class=\"data row4 col2 false \" >73.2</td>\n",
       "      <td id=\"T_82515_row4_col3\" class=\"data row4 col3 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row4_col4\" class=\"data row4 col4 false \" >0.0</td>\n",
       "      <td id=\"T_82515_row4_col5\" class=\"data row4 col5 true \" >0.0</td>\n",
       "      <td id=\"T_82515_row4_col6\" class=\"data row4 col6\" >3.5</td>\n",
       "      <td id=\"T_82515_row4_col7\" class=\"data row4 col7\" >95.4</td>\n",
       "      <td id=\"T_82515_row4_col8\" class=\"data row4 col8\" >59.4</td>\n",
       "      <td id=\"T_82515_row4_col9\" class=\"data row4 col9\" >0.0</td>\n",
       "      <td id=\"T_82515_row4_col10\" class=\"data row4 col10\" >0.0</td>\n",
       "      <td id=\"T_82515_row4_col11\" class=\"data row4 col11\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_82515_level0_row5\" class=\"row_heading level0 row5\" >max</th>\n",
       "      <td id=\"T_82515_row5_col0\" class=\"data row5 col0 false \" >88.5</td>\n",
       "      <td id=\"T_82515_row5_col1\" class=\"data row5 col1 false \" >100.0</td>\n",
       "      <td id=\"T_82515_row5_col2\" class=\"data row5 col2 false \" >100.0</td>\n",
       "      <td id=\"T_82515_row5_col3\" class=\"data row5 col3 false \" >99.0</td>\n",
       "      <td id=\"T_82515_row5_col4\" class=\"data row5 col4 false \" >10.5</td>\n",
       "      <td id=\"T_82515_row5_col5\" class=\"data row5 col5 true \" >2.0</td>\n",
       "      <td id=\"T_82515_row5_col6\" class=\"data row5 col6\" >99.0</td>\n",
       "      <td id=\"T_82515_row5_col7\" class=\"data row5 col7\" >100.0</td>\n",
       "      <td id=\"T_82515_row5_col8\" class=\"data row5 col8\" >100.0</td>\n",
       "      <td id=\"T_82515_row5_col9\" class=\"data row5 col9\" >99.5</td>\n",
       "      <td id=\"T_82515_row5_col10\" class=\"data row5 col10\" >18.0</td>\n",
       "      <td id=\"T_82515_row5_col11\" class=\"data row5 col11\" >0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd96cc8b7c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on 60 Clark Center Survey questions. The Economics Profs Survey panel (top left) gives the distribution of the share of \n",
      "respondents who chose a given answer, across questions. The other panels give the distribution of answers of various versions of ChatGPT, \n",
      "when querried 200 times, across questions. ChatGPT 4oProf is ChatGPT 4o with an Economics Prof as persona.\n"
     ]
    }
   ],
   "source": [
    "# above we put all info together, now we create table I\n",
    "a=togetheropinion[['Strongly Agree','Agree','Uncertain', 'Disagree','Strongly Disagree','No Opinion']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "b= GPT35overview[['Strongly Agree Chatgpt','Agree Chatgpt','Uncertain Chatgpt', 'Disagree Chatgpt','Strongly Disagree Chatgpt','No Opinion Chatgpt']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "clean=a.columns\n",
    "\n",
    "b.columns=clean\n",
    "a=pd.concat([a], keys=['Economics Profs Survey'], axis=1)\n",
    "b=pd.concat([b], keys=['ChatGPT 3.5'], axis=1)\n",
    "\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true ']],\n",
    "                          index=a.index,\n",
    "                          columns=a.columns)\n",
    "\n",
    "\n",
    "\n",
    "display(pd.concat([a,b], axis=1).style.format(precision=1).set_caption(\"Table 1: Distribution of Answers - Economics Prof Survey vs. ChatGPT\").set_table_styles(styles).set_td_classes(cell_color))\n",
    "\n",
    "c= GPT4overview[['Strongly Agree Chatgpt','Agree Chatgpt','Uncertain Chatgpt', 'Disagree Chatgpt','Strongly Disagree Chatgpt','No Opinion Chatgpt']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "d= GPT4Profoverview[['Strongly Agree Chatgpt','Agree Chatgpt','Uncertain Chatgpt', 'Disagree Chatgpt','Strongly Disagree Chatgpt','No Opinion Chatgpt']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "c.columns=clean\n",
    "d.columns=clean\n",
    "c=pd.concat([c], keys=['ChatGPT 4o'], axis=1)\n",
    "d=pd.concat([d], keys=['ChatGPT 4oProf'], axis=1)\n",
    "\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true ']],\n",
    "                          index=c.index,\n",
    "                          columns=c.columns)\n",
    "\n",
    "\n",
    "\n",
    "display(pd.concat([c,d], axis=1).style.format(precision=1).set_table_styles(styles).set_td_classes(cell_color))\n",
    "print('Notes: this table is based on 60 Clark Center Survey questions. The Economics Profs Survey panel (top left) gives the distribution of the share of \\nrespondents who chose a given answer, across questions. The other panels give the distribution of answers of various versions of ChatGPT, \\nwhen querried 200 times, across questions. ChatGPT 4oProf is ChatGPT 4o with an Economics Prof as persona.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3af4bd8b-5717-4053-bbbd-1d039d842ea3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a22d4 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_a22d4 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a22d4 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a22d4 .true {\n",
       "  color: blue;\n",
       "}\n",
       "#T_a22d4 .true2 {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_a22d4 .true3 {\n",
       "  color: blue;\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a22d4\">\n",
       "  <caption>Table 2: Cross-Tabulation of Most Common Answer, Counts - Economics Profs vs. ChatGPT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a22d4_level0_col0\" class=\"col_heading level0 col0\" colspan=\"6\">ChatGPT 3.5</th>\n",
       "      <th id=\"T_a22d4_level0_col6\" class=\"col_heading level0 col6\" colspan=\"6\">ChatGPT 4o</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_a22d4_level1_col0\" class=\"col_heading level1 col0\" >Strongly Agree</th>\n",
       "      <th id=\"T_a22d4_level1_col1\" class=\"col_heading level1 col1\" >Agree</th>\n",
       "      <th id=\"T_a22d4_level1_col2\" class=\"col_heading level1 col2\" >Uncertain</th>\n",
       "      <th id=\"T_a22d4_level1_col3\" class=\"col_heading level1 col3\" >Disagree</th>\n",
       "      <th id=\"T_a22d4_level1_col4\" class=\"col_heading level1 col4\" >Strongly Disagree</th>\n",
       "      <th id=\"T_a22d4_level1_col5\" class=\"col_heading level1 col5\" >No Opinion</th>\n",
       "      <th id=\"T_a22d4_level1_col6\" class=\"col_heading level1 col6\" >Strongly Agree</th>\n",
       "      <th id=\"T_a22d4_level1_col7\" class=\"col_heading level1 col7\" >Agree</th>\n",
       "      <th id=\"T_a22d4_level1_col8\" class=\"col_heading level1 col8\" >Uncertain</th>\n",
       "      <th id=\"T_a22d4_level1_col9\" class=\"col_heading level1 col9\" >Disagree</th>\n",
       "      <th id=\"T_a22d4_level1_col10\" class=\"col_heading level1 col10\" >Strongly Disagree</th>\n",
       "      <th id=\"T_a22d4_level1_col11\" class=\"col_heading level1 col11\" >No Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"6\">Economics Profs</th>\n",
       "      <th id=\"T_a22d4_level1_row0\" class=\"row_heading level1 row0\" >Strongly Agree</th>\n",
       "      <td id=\"T_a22d4_row0_col0\" class=\"data row0 col0 true \" >2</td>\n",
       "      <td id=\"T_a22d4_row0_col1\" class=\"data row0 col1 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col2\" class=\"data row0 col2 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col3\" class=\"data row0 col3 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col4\" class=\"data row0 col4 false \" >1</td>\n",
       "      <td id=\"T_a22d4_row0_col5\" class=\"data row0 col5 true2 \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col6\" class=\"data row0 col6 true \" >1</td>\n",
       "      <td id=\"T_a22d4_row0_col7\" class=\"data row0 col7 false \" >2</td>\n",
       "      <td id=\"T_a22d4_row0_col8\" class=\"data row0 col8 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col9\" class=\"data row0 col9 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col10\" class=\"data row0 col10 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row0_col11\" class=\"data row0 col11 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level1_row1\" class=\"row_heading level1 row1\" >Agree</th>\n",
       "      <td id=\"T_a22d4_row1_col0\" class=\"data row1 col0 false\" >11</td>\n",
       "      <td id=\"T_a22d4_row1_col1\" class=\"data row1 col1 true \" >16</td>\n",
       "      <td id=\"T_a22d4_row1_col2\" class=\"data row1 col2 false \" >1</td>\n",
       "      <td id=\"T_a22d4_row1_col3\" class=\"data row1 col3 false \" >2</td>\n",
       "      <td id=\"T_a22d4_row1_col4\" class=\"data row1 col4 false \" >8</td>\n",
       "      <td id=\"T_a22d4_row1_col5\" class=\"data row1 col5 true2 \" >0</td>\n",
       "      <td id=\"T_a22d4_row1_col6\" class=\"data row1 col6 false\" >2</td>\n",
       "      <td id=\"T_a22d4_row1_col7\" class=\"data row1 col7 true \" >26</td>\n",
       "      <td id=\"T_a22d4_row1_col8\" class=\"data row1 col8 false \" >8</td>\n",
       "      <td id=\"T_a22d4_row1_col9\" class=\"data row1 col9 false \" >2</td>\n",
       "      <td id=\"T_a22d4_row1_col10\" class=\"data row1 col10 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row1_col11\" class=\"data row1 col11 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level1_row2\" class=\"row_heading level1 row2\" >Uncertain</th>\n",
       "      <td id=\"T_a22d4_row2_col0\" class=\"data row2 col0 false \" >3</td>\n",
       "      <td id=\"T_a22d4_row2_col1\" class=\"data row2 col1 false \" >11</td>\n",
       "      <td id=\"T_a22d4_row2_col2\" class=\"data row2 col2 true \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col3\" class=\"data row2 col3 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col4\" class=\"data row2 col4 false \" >3</td>\n",
       "      <td id=\"T_a22d4_row2_col5\" class=\"data row2 col5 true2 \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col6\" class=\"data row2 col6 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col7\" class=\"data row2 col7 false \" >7</td>\n",
       "      <td id=\"T_a22d4_row2_col8\" class=\"data row2 col8 true \" >10</td>\n",
       "      <td id=\"T_a22d4_row2_col9\" class=\"data row2 col9 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col10\" class=\"data row2 col10 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row2_col11\" class=\"data row2 col11 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level1_row3\" class=\"row_heading level1 row3\" >Disagree</th>\n",
       "      <td id=\"T_a22d4_row3_col0\" class=\"data row3 col0 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col1\" class=\"data row3 col1 false \" >2</td>\n",
       "      <td id=\"T_a22d4_row3_col2\" class=\"data row3 col2 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col3\" class=\"data row3 col3 true \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col4\" class=\"data row3 col4 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col5\" class=\"data row3 col5 true2 \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col6\" class=\"data row3 col6 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col7\" class=\"data row3 col7 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col8\" class=\"data row3 col8 false \" >2</td>\n",
       "      <td id=\"T_a22d4_row3_col9\" class=\"data row3 col9 true \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col10\" class=\"data row3 col10 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row3_col11\" class=\"data row3 col11 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level1_row4\" class=\"row_heading level1 row4\" >Strongly Disagree</th>\n",
       "      <td id=\"T_a22d4_row4_col0\" class=\"data row4 col0 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col1\" class=\"data row4 col1 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col2\" class=\"data row4 col2 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col3\" class=\"data row4 col3 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col4\" class=\"data row4 col4 true \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col5\" class=\"data row4 col5 true2 \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col6\" class=\"data row4 col6 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col7\" class=\"data row4 col7 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col8\" class=\"data row4 col8 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col9\" class=\"data row4 col9 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col10\" class=\"data row4 col10 true \" >0</td>\n",
       "      <td id=\"T_a22d4_row4_col11\" class=\"data row4 col11 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a22d4_level1_row5\" class=\"row_heading level1 row5\" >No Opinion</th>\n",
       "      <td id=\"T_a22d4_row5_col0\" class=\"data row5 col0 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col1\" class=\"data row5 col1 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col2\" class=\"data row5 col2 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col3\" class=\"data row5 col3 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col4\" class=\"data row5 col4 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col5\" class=\"data row5 col5 true3 \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col6\" class=\"data row5 col6 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col7\" class=\"data row5 col7 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col8\" class=\"data row5 col8 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col9\" class=\"data row5 col9 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col10\" class=\"data row5 col10 false \" >0</td>\n",
       "      <td id=\"T_a22d4_row5_col11\" class=\"data row5 col11 true \" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_5725d_level0_col0\" class=\"col_heading level0 col0\" colspan=\"6\">ChatGPT 4oProf</th>\n",
       "    </tr>\n",
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       "      <th id=\"T_5725d_level1_col0\" class=\"col_heading level1 col0\" >Strongly Agree</th>\n",
       "      <th id=\"T_5725d_level1_col1\" class=\"col_heading level1 col1\" >Agree</th>\n",
       "      <th id=\"T_5725d_level1_col2\" class=\"col_heading level1 col2\" >Uncertain</th>\n",
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       "      <th id=\"T_5725d_level1_col5\" class=\"col_heading level1 col5\" >No Opinion</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_5725d_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"6\">Economics Profs</th>\n",
       "      <th id=\"T_5725d_level1_row0\" class=\"row_heading level1 row0\" >Strongly Agree</th>\n",
       "      <td id=\"T_5725d_row0_col0\" class=\"data row0 col0 true \" >3</td>\n",
       "      <td id=\"T_5725d_row0_col1\" class=\"data row0 col1 false \" >0</td>\n",
       "      <td id=\"T_5725d_row0_col2\" class=\"data row0 col2 false \" >0</td>\n",
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       "      <td id=\"T_5725d_row0_col5\" class=\"data row0 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_5725d_level1_row1\" class=\"row_heading level1 row1\" >Agree</th>\n",
       "      <td id=\"T_5725d_row1_col0\" class=\"data row1 col0 false\" >4</td>\n",
       "      <td id=\"T_5725d_row1_col1\" class=\"data row1 col1 true \" >27</td>\n",
       "      <td id=\"T_5725d_row1_col2\" class=\"data row1 col2 false \" >5</td>\n",
       "      <td id=\"T_5725d_row1_col3\" class=\"data row1 col3 false \" >2</td>\n",
       "      <td id=\"T_5725d_row1_col4\" class=\"data row1 col4 false \" >0</td>\n",
       "      <td id=\"T_5725d_row1_col5\" class=\"data row1 col5 false \" >0</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_5725d_level1_row2\" class=\"row_heading level1 row2\" >Uncertain</th>\n",
       "      <td id=\"T_5725d_row2_col0\" class=\"data row2 col0 false \" >0</td>\n",
       "      <td id=\"T_5725d_row2_col1\" class=\"data row2 col1 false \" >7</td>\n",
       "      <td id=\"T_5725d_row2_col2\" class=\"data row2 col2 true \" >10</td>\n",
       "      <td id=\"T_5725d_row2_col3\" class=\"data row2 col3 false \" >0</td>\n",
       "      <td id=\"T_5725d_row2_col4\" class=\"data row2 col4 false \" >0</td>\n",
       "      <td id=\"T_5725d_row2_col5\" class=\"data row2 col5 false \" >0</td>\n",
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       "      <th id=\"T_5725d_level1_row3\" class=\"row_heading level1 row3\" >Disagree</th>\n",
       "      <td id=\"T_5725d_row3_col0\" class=\"data row3 col0 false \" >0</td>\n",
       "      <td id=\"T_5725d_row3_col1\" class=\"data row3 col1 false \" >0</td>\n",
       "      <td id=\"T_5725d_row3_col2\" class=\"data row3 col2 false \" >2</td>\n",
       "      <td id=\"T_5725d_row3_col3\" class=\"data row3 col3 true \" >0</td>\n",
       "      <td id=\"T_5725d_row3_col4\" class=\"data row3 col4 false \" >0</td>\n",
       "      <td id=\"T_5725d_row3_col5\" class=\"data row3 col5 false \" >0</td>\n",
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       "      <th id=\"T_5725d_level1_row4\" class=\"row_heading level1 row4\" >Strongly Disagree</th>\n",
       "      <td id=\"T_5725d_row4_col0\" class=\"data row4 col0 false \" >0</td>\n",
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     "text": [
      "Notes: this table is based on 60 Clark Center Survey questions. \n",
      "Horizontal shows the answer of the Economics Profs Survey, vertical is the answer of ChatGPT.\n",
      "The diagonal (in blue) gives the number of times the Economics Profs Survey and ChatGPT have the same most frequent answer. \n",
      "Off-diagional elements show counts of differences in most frequent answers. \n",
      "ChatGPT 4oProf is ChatGPT 4o with an Economics Prof as persona.\n"
     ]
    }
   ],
   "source": [
    "# Now compute how many times we get same 'most frequent' answer\n",
    "# first stat: cross tab of answers to show to what extent PRofs and GPT give same answer\n",
    "# we focus here on US/Euro profs together compared to various ChatGPTs\n",
    "# here 3.5\n",
    "byquestioncross=pd.DataFrame(0,index=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'], columns=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'])\n",
    "byquestioncross\n",
    "for i in range(0,60):\n",
    "    byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT3.5 Median'][i].title()]=byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT3.5 Median'][i].title()]+1\n",
    "          \n",
    "\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economics Profs'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 3.5'], axis=1)\n",
    "\n",
    "byquestioncross35=byquestioncross\n",
    "\n",
    "# first stat: cross tab of answers to show to what extent PRofs and GPT give same answer\n",
    "# we focus here on US/Euro profs together compared to various ChatGPTs\n",
    "# second 4o\n",
    "byquestioncross=pd.DataFrame(0,index=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'], columns=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'])\n",
    "byquestioncross\n",
    "\n",
    "for i in range(0,60):\n",
    "    byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT4o Median'][i].title()]=byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT4o Median'][i].title()]+1\n",
    "\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economics Profs'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 4o'], axis=1)\n",
    "byquestioncross4o=byquestioncross\n",
    "\n",
    "# first stat: cross tab of answers to show to what extent PRofs and GPT give same answer\n",
    "# we focus here on US/Euro profs together compared to various ChatGPTs\n",
    "# third 4o Prof\n",
    "byquestioncross=pd.DataFrame(0,index=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'], columns=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'])\n",
    "byquestioncross\n",
    "for i in range(0,60):\n",
    "    byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT4oProf Median'][i].title()]=byquestioncross.at[allstats['Euro + US Median'][i].title(),allstats['GPT4oProf Median'][i].title()]+1\n",
    "\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economics Profs'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 4oProf'], axis=1)\n",
    "\n",
    "byquestioncross4oprof=byquestioncross\n",
    "\n",
    "a=pd.concat([byquestioncross35,byquestioncross4o], axis=1)\n",
    "# styles for tables\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'},\n",
    "        {'selector': '.true2', 'props': 'border-right:solid; border-color:blue; vertical-align:top'},\n",
    "         {'selector': '.true3', 'props': 'color: blue;border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([['true ', 'false ', 'false ', 'false ', 'false ', 'true2 ','true ', 'false ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false', 'true ', 'false ', 'false ', 'false ', 'true2 ','false', 'true ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'true ', 'false ', 'false ', 'true2 ','false ', 'false ', 'true ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'true ', 'false ', 'true2 ','false ', 'false ', 'false ', 'true ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'true ', 'true2 ','false ', 'false ', 'false ', 'false ', 'true ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true3 ','false ', 'false ', 'false ', 'false ', 'false ', 'true ']],\n",
    "                          index=a.index,\n",
    "                          columns=a.columns)\n",
    "\n",
    "\n",
    "display(a.style.set_caption(\"Table 2: Cross-Tabulation of Most Common Answer, Counts - Economics Profs vs. ChatGPT\").set_table_styles(styles).set_td_classes(cell_color))\n",
    "\n",
    "cell_color = pd.DataFrame([['true ', 'false ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false', 'true ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'true ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'true ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'true ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true ']],\n",
    "                          index=byquestioncross4oprof.index,\n",
    "                          columns=byquestioncross4oprof.columns)\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center; width: 68px'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'},\n",
    "        {'selector': '.true2', 'props': 'border-right:solid; border-color:blue; vertical-align:top'},\n",
    "         {'selector': '.true3', 'props': 'color: blue;border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "display(byquestioncross4oprof.style.set_table_styles(styles).set_td_classes(cell_color).set_properties(**{'width': '10px'}))\n",
    "print('Notes: this table is based on 60 Clark Center Survey questions. \\nHorizontal shows the answer of the Economics Profs Survey, vertical is the answer of ChatGPT.\\nThe diagonal (in blue) gives the number of times the Economics Profs Survey and ChatGPT have the same most frequent answer. \\nOff-diagional elements show counts of differences in most frequent answers. \\nChatGPT 4oProf is ChatGPT 4o with an Economics Prof as persona.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e47647f-f84f-4c79-ab8c-0d4d8674262d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "953ea27d-b57e-417d-a084-c9a6dcb3090a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "  text-align: center;\n",
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       "  <caption>US Economics Profs versus Euro + US Economic Profs</caption>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <td id=\"T_2e785_row0_col3\" class=\"data row0 col3 false \" >0</td>\n",
       "      <td id=\"T_2e785_row0_col4\" class=\"data row0 col4 false \" >0</td>\n",
       "      <td id=\"T_2e785_row0_col5\" class=\"data row0 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e785_level1_row1\" class=\"row_heading level1 row1\" >Agree</th>\n",
       "      <td id=\"T_2e785_row1_col0\" class=\"data row1 col0 false\" >0</td>\n",
       "      <td id=\"T_2e785_row1_col1\" class=\"data row1 col1 true \" >32</td>\n",
       "      <td id=\"T_2e785_row1_col2\" class=\"data row1 col2 false \" >4</td>\n",
       "      <td id=\"T_2e785_row1_col3\" class=\"data row1 col3 false \" >1</td>\n",
       "      <td id=\"T_2e785_row1_col4\" class=\"data row1 col4 false \" >0</td>\n",
       "      <td id=\"T_2e785_row1_col5\" class=\"data row1 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e785_level1_row2\" class=\"row_heading level1 row2\" >Uncertain</th>\n",
       "      <td id=\"T_2e785_row2_col0\" class=\"data row2 col0 false \" >0</td>\n",
       "      <td id=\"T_2e785_row2_col1\" class=\"data row2 col1 false \" >6</td>\n",
       "      <td id=\"T_2e785_row2_col2\" class=\"data row2 col2 true \" >13</td>\n",
       "      <td id=\"T_2e785_row2_col3\" class=\"data row2 col3 false \" >1</td>\n",
       "      <td id=\"T_2e785_row2_col4\" class=\"data row2 col4 false \" >0</td>\n",
       "      <td id=\"T_2e785_row2_col5\" class=\"data row2 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e785_level1_row3\" class=\"row_heading level1 row3\" >Disagree</th>\n",
       "      <td id=\"T_2e785_row3_col0\" class=\"data row3 col0 false \" >0</td>\n",
       "      <td id=\"T_2e785_row3_col1\" class=\"data row3 col1 false \" >0</td>\n",
       "      <td id=\"T_2e785_row3_col2\" class=\"data row3 col2 false \" >0</td>\n",
       "      <td id=\"T_2e785_row3_col3\" class=\"data row3 col3 true \" >0</td>\n",
       "      <td id=\"T_2e785_row3_col4\" class=\"data row3 col4 false \" >0</td>\n",
       "      <td id=\"T_2e785_row3_col5\" class=\"data row3 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e785_level1_row4\" class=\"row_heading level1 row4\" >Strongly Disagree</th>\n",
       "      <td id=\"T_2e785_row4_col0\" class=\"data row4 col0 false \" >0</td>\n",
       "      <td id=\"T_2e785_row4_col1\" class=\"data row4 col1 false \" >0</td>\n",
       "      <td id=\"T_2e785_row4_col2\" class=\"data row4 col2 false \" >0</td>\n",
       "      <td id=\"T_2e785_row4_col3\" class=\"data row4 col3 false \" >0</td>\n",
       "      <td id=\"T_2e785_row4_col4\" class=\"data row4 col4 true \" >0</td>\n",
       "      <td id=\"T_2e785_row4_col5\" class=\"data row4 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2e785_level1_row5\" class=\"row_heading level1 row5\" >No Opinion</th>\n",
       "      <td id=\"T_2e785_row5_col0\" class=\"data row5 col0 false \" >0</td>\n",
       "      <td id=\"T_2e785_row5_col1\" class=\"data row5 col1 false \" >0</td>\n",
       "      <td id=\"T_2e785_row5_col2\" class=\"data row5 col2 false \" >0</td>\n",
       "      <td id=\"T_2e785_row5_col3\" class=\"data row5 col3 false \" >0</td>\n",
       "      <td id=\"T_2e785_row5_col4\" class=\"data row5 col4 false \" >0</td>\n",
       "      <td id=\"T_2e785_row5_col5\" class=\"data row5 col5 true \" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f938b455cd0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for comparison - US versus Euro\n",
    "byquestioncross=pd.DataFrame(0,index=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'], columns=['Strongly Agree','Agree', 'Uncertain', 'Disagree', 'Strongly Disagree','No Opinion'])\n",
    "byquestioncross\n",
    "\n",
    "for i in range(0,60):\n",
    "    byquestioncross.at[allstats['US Median'][i].title(),allstats['Euro + US Median'][i].title()]=byquestioncross.at[allstats['US Median'][i].title(),allstats['Euro + US Median'][i].title()]+1\n",
    "byquestioncross\n",
    "\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['US Economics Profs'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Euro + US Economics Profs'], axis=1)\n",
    "cell_color = pd.DataFrame([['true ', 'false ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false', 'true ', 'false ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'true ', 'false ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'true ', 'false ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'true ', 'false '],\n",
    "                           ['false ', 'false ', 'false ', 'false ', 'false ', 'true ']],\n",
    "                          index=byquestioncross.index,\n",
    "                          columns=byquestioncross.columns)\n",
    "\n",
    "byquestioncross.style.set_caption(\"US Economics Profs versus Euro + US Economic Profs\").set_table_styles(styles).set_td_classes(cell_color)\n",
    "\n",
    "\n",
    "# not disagree EU versus agree US are in q 4 and 25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "269d3177-d3ef-48bf-a4bb-324bbaeef241",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_04726 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_04726 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_04726 th {\n",
       "  text-align: center;\n",
       "  width: 68px;\n",
       "}\n",
       "#T_04726 .true {\n",
       "  color: blue;\n",
       "}\n",
       "#T_04726 .true2 {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_04726 .true3 {\n",
       "  color: blue;\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_04726_row0_col0, #T_04726_row0_col1, #T_04726_row0_col2, #T_04726_row1_col0, #T_04726_row1_col1, #T_04726_row1_col2, #T_04726_row2_col0, #T_04726_row2_col1, #T_04726_row2_col2, #T_04726_row3_col0, #T_04726_row3_col1, #T_04726_row3_col2 {\n",
       "  width: 300px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_04726\">\n",
       "  <caption>Table 3 - Economics Profs vs ChatGPT: When Asked On(c)e, Chance You Get ..., in %</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_04726_level0_col0\" class=\"col_heading level0 col0\" >Most Frequent Answer</th>\n",
       "      <th id=\"T_04726_level0_col1\" class=\"col_heading level0 col1\" >One Category Off Most Frequent Answer</th>\n",
       "      <th id=\"T_04726_level0_col2\" class=\"col_heading level0 col2\" >More Than One Category Off Most Frequent Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_04726_level0_row0\" class=\"row_heading level0 row0\" >Economics Profs Survey</th>\n",
       "      <td id=\"T_04726_row0_col0\" class=\"data row0 col0\" >50.1</td>\n",
       "      <td id=\"T_04726_row0_col1\" class=\"data row0 col1\" >41.2</td>\n",
       "      <td id=\"T_04726_row0_col2\" class=\"data row0 col2\" >8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04726_level0_row1\" class=\"row_heading level0 row1\" >GTP3.5</th>\n",
       "      <td id=\"T_04726_row1_col0\" class=\"data row1 col0\" >29.5</td>\n",
       "      <td id=\"T_04726_row1_col1\" class=\"data row1 col1\" >39.7</td>\n",
       "      <td id=\"T_04726_row1_col2\" class=\"data row1 col2\" >30.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04726_level0_row2\" class=\"row_heading level0 row2\" >GTP4o</th>\n",
       "      <td id=\"T_04726_row2_col0\" class=\"data row2 col0\" >60.4</td>\n",
       "      <td id=\"T_04726_row2_col1\" class=\"data row2 col1\" >35.0</td>\n",
       "      <td id=\"T_04726_row2_col2\" class=\"data row2 col2\" >4.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_04726_level0_row3\" class=\"row_heading level0 row3\" >GPT40Prof</th>\n",
       "      <td id=\"T_04726_row3_col0\" class=\"data row3 col0\" >60.8</td>\n",
       "      <td id=\"T_04726_row3_col1\" class=\"data row3 col1\" >34.7</td>\n",
       "      <td id=\"T_04726_row3_col2\" class=\"data row3 col2\" >4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f93cc451880>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on 60 Clark Center Survey questions. \n",
      "It gives the chance you get, on a specific question, the most frequent answer of the Economics Profs Survey, \n",
      "if you ask one Professor that question, or if you ask ChatGPT that question once\n"
     ]
    }
   ],
   "source": [
    "a=togetheropinion[['Chance Top Prof Choice','Chance GPT35 Top Prof Choice','Chance GPT4o Top Prof Choice','Chance GPT4oProf Top Prof Choice']]\n",
    "a.columns=['Economics Profs Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "a=a.describe().loc['mean']\n",
    "a=a.rename('Most Frequent Answer')\n",
    "\n",
    "b=togetheropinion[['Chance One Off Prof Choice','Chance GPT35 One Off Top Prof Choice','Chance GPT4o One Off Top Prof Choice','Chance GPT4oProf One Off Top Prof Choice']]\n",
    "b.columns=['Economics Profs Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "b=b.describe().loc['mean']\n",
    "b=b.rename('One Category Off Most Frequent Answer')\n",
    "\n",
    "c=togetheropinion[['Chance More than One Off Prof Choice','Chance GPT35 More than One Off Top Prof Choice','Chance GPT4o More than One Off Top Prof Choice','Chance GPT4oProf More than One Off Top Prof Choice']]\n",
    "c.columns=['Economics Profs Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "c=c.describe().loc['mean']\n",
    "c=c.rename('More Than One Category Off Most Frequent Answer')\n",
    "\n",
    "display(pd.concat([a,b,c], axis=1).style.set_caption(\"Table 3 - Economics Profs vs ChatGPT: When Asked On(c)e, Chance You Get ..., in %\").set_table_styles(styles).format(precision=1).set_properties(**{'width': '300px'}))\n",
    "print('Notes: this table is based on 60 Clark Center Survey questions. \\nIt gives the chance you get, on a specific question, the most frequent answer of the Economics Profs Survey, \\nif you ask one Professor that question, or if you ask ChatGPT that question once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "fce28af1-4fbe-4f46-b721-7dbe3f04758a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_40bfc caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_40bfc td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_40bfc th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_40bfc .true {\n",
       "  color: blue;\n",
       "}\n",
       "#T_40bfc .true2 {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_40bfc .true3 {\n",
       "  color: blue;\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_40bfc\">\n",
       "  <caption>Table A1a - Clark Center Claims</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_40bfc_level0_col0\" class=\"col_heading level0 col0\" > </th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_40bfc_row0_col0\" class=\"data row0 col0\" >A ban on advertising junk foods (those that are high in sugar, salt and fat) would be an effective policy to reduce child obesity.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_40bfc_row1_col0\" class=\"data row1 col0\" >A federal minimum wage that is pegged to state and/or local conditions such as the cost of living would be preferable to the current arrangements that give states a role in setting the policy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_40bfc_row2_col0\" class=\"data row2 col0\" >A global corporate tax system that is based on the location of final consumers would be more efficient than one based on the location of corporate headquarters and production facilities.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_40bfc_row3_col0\" class=\"data row3 col0\" >A global minimum corporate tax rate would limit the benefits to companies of shifting profits to low-tax jurisdictions without biasing where they invest.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_40bfc_row4_col0\" class=\"data row4 col0\" >A mandate for public companies to provide climate-related disclosures (such as their greenhouse gas emissions and carbon footprint) would provide financially material information that enables investors to make better decisions.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_40bfc_row5_col0\" class=\"data row5 col0\" >A mandate for public companies to provide climate-related disclosures would induce them to reduce their climate impact significantly.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_40bfc_row6_col0\" class=\"data row6 col0\" >A price cap imposed by the G7/EU countries on purchases of Russian oil and oil-related products (and which applies to all importers of Russian oil using Western trade infrastructure, shipping, and insurance) would be an effective measure to reduce the flow of revenues to Russia.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_40bfc_row7_col0\" class=\"data row7 col0\" >As of now, there needs to be more government regulation around Twitter’s content moderation and personal data protection.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_40bfc_row8_col0\" class=\"data row8 col0\" >Central banks that do not introduce their own digital money risk losing the ability to conduct effective monetary policy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_40bfc_row9_col0\" class=\"data row9 col0\" >Employees who spend two of their days each week working from home are, on average, likely to be more productive over the longer term.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_40bfc_row10_col0\" class=\"data row10 col0\" >Employees who spend two of their days each week working from home are, on average, likely to report higher levels of job satisfaction over the longer term.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_40bfc_row11_col0\" class=\"data row11 col0\" >Having the opportunity to work two to three days a week from home is, on average, like to be more beneficial for women’s career progression than for that of their male colleagues.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_40bfc_row12_col0\" class=\"data row12 col0\" >High tariffs imposed by the European Union on imports of Russian natural gas would be an effective measure to reduce the flow of revenues to Russia while limiting disruption to supplies to Europe.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_40bfc_row13_col0\" class=\"data row13 col0\" >In pursuit of credible research designs, researchers often seek good answers instead of good questions.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_40bfc_row14_col0\" class=\"data row14 col0\" >Network externalities give Twitter an incumbent advantage that will slow substantially the migration of users who would prefer alternative platforms.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_40bfc_row15_col0\" class=\"data row15 col0\" >Policies that aim to reduce obesity by increasing incentives for physical activity would improve social welfare more than policies that increase the financial costs of consuming calories.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
       "      <td id=\"T_40bfc_row16_col0\" class=\"data row16 col0\" >Stablecoins that are not fully backed by either central bank reserves or government securities with minimal price volatility are inherently vulnerable to runs.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_40bfc_row17_col0\" class=\"data row17 col0\" >Targeting the Russian economy through a total ban on oil and gas imports carries a high risk of recession in European economies.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
       "      <td id=\"T_40bfc_row18_col0\" class=\"data row18 col0\" >The Bank for International Settlements defines a central bank digital currency as follows: ‘In simple terms, a central bank digital currency (CBDC) would be a digital banknote. It could be used by individuals to pay businesses, shops or each other (a 'retail CBDC'), or between financial institutions to settle trades in financial markets  For developed countries, a central bank digital currency that is available to the public at large would offer social benefits that exceed the associated costs or risks.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_40bfc_row19_col0\" class=\"data row19 col0\" >The UK economy is likely to be at least several percentage points smaller in 2030 than it would have been if the country had remained in the European Union.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "      <td id=\"T_40bfc_row20_col0\" class=\"data row20 col0\" >The aggregate economy of the 27 countries still in the EU is likely to be at least several percentage points smaller in 2030 than if the UK had not left.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row21\" class=\"row_heading level0 row21\" >22</th>\n",
       "      <td id=\"T_40bfc_row21_col0\" class=\"data row21 col0\" >The current US federal minimum wage is $7.25 per hour. States can choose whether to have a higher minimum - and many do.A federal minimum wage of $15 per hour would lower employment for low-wage workers in many states.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row22\" class=\"row_heading level0 row22\" >23</th>\n",
       "      <td id=\"T_40bfc_row22_col0\" class=\"data row22 col0\" >The economic and financial sanctions already implemented will lead to a deep recession in Russia.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row23\" class=\"row_heading level0 row23\" >24</th>\n",
       "      <td id=\"T_40bfc_row23_col0\" class=\"data row23 col0\" >The introduction of a central bank digital currency is unlikely to have major effects on the economy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row24\" class=\"row_heading level0 row24\" >25</th>\n",
       "      <td id=\"T_40bfc_row24_col0\" class=\"data row24 col0\" >The introduction of natural experiments to economic analysis of the labor market and related areas has led to a more precise understanding of cause and effect.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row25\" class=\"row_heading level0 row25\" >26</th>\n",
       "      <td id=\"T_40bfc_row25_col0\" class=\"data row25 col0\" >The oil price cap imposed by the G7/EU countries will not have a substantial effect on the world oil price (such as the Brent crude benchmark).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row26\" class=\"row_heading level0 row26\" >27</th>\n",
       "      <td id=\"T_40bfc_row26_col0\" class=\"data row26 col0\" >The ‘credibility revolution’ in empirical economics has improved our understanding of a number of public policy issues, including education, immigration and the minimum wage.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row27\" class=\"row_heading level0 row27\" >28</th>\n",
       "      <td id=\"T_40bfc_row27_col0\" class=\"data row27 col0\" >Artificial intelligence is likely to be a highly concentrated industry, dominated by a handful of players.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row28\" class=\"row_heading level0 row28\" >29</th>\n",
       "      <td id=\"T_40bfc_row28_col0\" class=\"data row28 col0\" >Artificial intelligence offers substantial opportunities for new entrants into digital markets that have previously been concentrated.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row29\" class=\"row_heading level0 row29\" >30</th>\n",
       "      <td id=\"T_40bfc_row29_col0\" class=\"data row29 col0\" >By enabling women’s life choices about education, work and family, the contraceptive pill made a substantial contribution to closing gender gaps in the labor market for professionals.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row30\" class=\"row_heading level0 row30\" >31</th>\n",
       "      <td id=\"T_40bfc_row30_col0\" class=\"data row30 col0\" >Even if Argentina could marshal the resources to make a full switch to using US dollars for domestic transactions, it would substantially increase the volatility of Argentine GDP.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row31\" class=\"row_heading level0 row31\" >32</th>\n",
       "      <td id=\"T_40bfc_row31_col0\" class=\"data row31 col0\" >Financial regulators in the US and Europe lack the tools and authority to deter runs on banks by uninsured depositors.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row32\" class=\"row_heading level0 row32\" >33</th>\n",
       "      <td id=\"T_40bfc_row32_col0\" class=\"data row32 col0\" >Fiscal rules on budget deficits and public debt levels are an essential part of a sound fiscal framework.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row33\" class=\"row_heading level0 row33\" >34</th>\n",
       "      <td id=\"T_40bfc_row33_col0\" class=\"data row33 col0\" >Fully guaranteeing uninsured deposits at Silicon Valley Bank substantially increases banks’ incentives to engage in excessive risk-taking.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row34\" class=\"row_heading level0 row34\" >35</th>\n",
       "      <td id=\"T_40bfc_row34_col0\" class=\"data row34 col0\" >Gender gaps in today’s labor market arise less from differences in educational and occupational choices than from the differential career impact of parenthood and social norms around men's and women’s roles in childrearing.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row35\" class=\"row_heading level0 row35\" >36</th>\n",
       "      <td id=\"T_40bfc_row35_col0\" class=\"data row35 col0\" >Given current regulations, non-bank financial intermediaries should not have access to central bank support.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row36\" class=\"row_heading level0 row36\" >37</th>\n",
       "      <td id=\"T_40bfc_row36_col0\" class=\"data row36 col0\" >In the absence of continuing flows of Western economic aid, Ukraine's wartime economy will be substantially compromised.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row37\" class=\"row_heading level0 row37\" >38</th>\n",
       "      <td id=\"T_40bfc_row37_col0\" class=\"data row37 col0\" >Non-bank financial intermediaries pose a substantial threat to financial stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row38\" class=\"row_heading level0 row38\" >39</th>\n",
       "      <td id=\"T_40bfc_row38_col0\" class=\"data row38 col0\" >Not guaranteeing uninsured deposits at Silicon Valley Bank in full would have created substantial damage to the US economy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row39\" class=\"row_heading level0 row39\" >40</th>\n",
       "      <td id=\"T_40bfc_row39_col0\" class=\"data row39 col0\" >Regulating the leverage and liquidity of non-bank financial intermediaries would substantially improve financial stability.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row40\" class=\"row_heading level0 row40\" >41</th>\n",
       "      <td id=\"T_40bfc_row40_col0\" class=\"data row40 col0\" >Responses To Market Power Constraints on the anti-competitive behavior of dominant firms in the digital economy can in principle be effectively implemented using the existing tools of competition policy and antitrust enforcement.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row41\" class=\"row_heading level0 row41\" >42</th>\n",
       "      <td id=\"T_40bfc_row41_col0\" class=\"data row41 col0\" >Since the inception of the Stability and Growth Pact, budget deficits in Europe have been measurably lower, on average, than would have been the case without common budget rules.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row42\" class=\"row_heading level0 row42\" >43</th>\n",
       "      <td id=\"T_40bfc_row42_col0\" class=\"data row42 col0\" >Since the inception of the Stability and Growth Pact, the path of GDP growth in Europe has been measurably more stable than would have been the case without common budget rules.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row43\" class=\"row_heading level0 row43\" >44</th>\n",
       "      <td id=\"T_40bfc_row43_col0\" class=\"data row43 col0\" >Subsidizing Green Technology Government subsidies for investment in green technologies are justified by substantial benefits coming from reducing unpriced carbon emissions and generating positive R&D spillovers.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row44\" class=\"row_heading level0 row44\" >45</th>\n",
       "      <td id=\"T_40bfc_row44_col0\" class=\"data row44 col0\" >The economic and financial sanctions against Russia are substantially limiting its ability to wage war on Ukraine.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row45\" class=\"row_heading level0 row45\" >46</th>\n",
       "      <td id=\"T_40bfc_row45_col0\" class=\"data row45 col0\" >The effectiveness of existing antitrust regimes in constraining anti-competitive behavior is substantially limited by the inadequacy of the resources available to competition and regulatory agencies relative to the dominant firms of the digital economy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row46\" class=\"row_heading level0 row46\" >47</th>\n",
       "      <td id=\"T_40bfc_row46_col0\" class=\"data row46 col0\" >The fundamental cause of Argentina’s high inflation is unfunded fiscal commitments that are being financed by the central bank.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row47\" class=\"row_heading level0 row47\" >48</th>\n",
       "      <td id=\"T_40bfc_row47_col0\" class=\"data row47 col0\" >The gender gap in pay would be substantially reduced if firms had fewer incentives to offer disproportionate rewards to individuals who work long and/or inflexible hours.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row48\" class=\"row_heading level0 row48\" >49</th>\n",
       "      <td id=\"T_40bfc_row48_col0\" class=\"data row48 col0\" >The proposed US tariffs on Chinese EVs would lead to measurably higher employment in the US automotive industry over the next five years.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row49\" class=\"row_heading level0 row49\" >50</th>\n",
       "      <td id=\"T_40bfc_row49_col0\" class=\"data row49 col0\" >The proposed US tariffs on Chinese EVs would measurably slow the adoption of green technology by consumers.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row50\" class=\"row_heading level0 row50\" >51</th>\n",
       "      <td id=\"T_40bfc_row50_col0\" class=\"data row50 col0\" >The response to recent bank failures should be to: Expand central banks’ lender of last resort facilities for banks.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row51\" class=\"row_heading level0 row51\" >52</th>\n",
       "      <td id=\"T_40bfc_row51_col0\" class=\"data row51 col0\" >The response to recent bank failures should be to: Substantially increase bank capital requirements.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row52\" class=\"row_heading level0 row52\" >53</th>\n",
       "      <td id=\"T_40bfc_row52_col0\" class=\"data row52 col0\" >The response to recent bank failures should be to: Substantially increase the limit on bank deposit insurance.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row53\" class=\"row_heading level0 row53\" >54</th>\n",
       "      <td id=\"T_40bfc_row53_col0\" class=\"data row53 col0\" >Use of artificial intelligence is likely to lead to a substantial increase in problems associated with market power in digital markets.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row54\" class=\"row_heading level0 row54\" >55</th>\n",
       "      <td id=\"T_40bfc_row54_col0\" class=\"data row54 col0\" >Use of artificial intelligence over the next ten years is likely to have a measurable impact in increasing income inequality.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row55\" class=\"row_heading level0 row55\" >56</th>\n",
       "      <td id=\"T_40bfc_row55_col0\" class=\"data row55 col0\" >Use of artificial intelligence over the next ten years will have a negative impact on the earnings potential of substantial numbers of high-skilled workers in advanced countries.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row56\" class=\"row_heading level0 row56\" >57</th>\n",
       "      <td id=\"T_40bfc_row56_col0\" class=\"data row56 col0\" >Use of artificial intelligence over the next ten years will have a substantially bigger impact on the growth rates of real per capita income in the US and Western Europe over the subsequent two decades than the internet has had over the past two decades.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row57\" class=\"row_heading level0 row57\" >58</th>\n",
       "      <td id=\"T_40bfc_row57_col0\" class=\"data row57 col0\" >Use of artificial intelligence over the next ten years will lead to a substantial increase in the growth rates of real per capita income in the US and Western Europe over the subsequent two decades.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row58\" class=\"row_heading level0 row58\" >59</th>\n",
       "      <td id=\"T_40bfc_row58_col0\" class=\"data row58 col0\" >Use of artificial intelligence over the next ten years will lead to substantially greater uncertainty about the likely returns to investment in education.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_40bfc_level0_row59\" class=\"row_heading level0 row59\" >60</th>\n",
       "      <td id=\"T_40bfc_row59_col0\" class=\"data row59 col0\" >Using subsidies for green technologies instead of full carbon prices will lead to substantially more rent-seeking and hence substantially higher costs to achieve a given reduction in emissions.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "<pandas.io.formats.style.Styler at 0x7fcd7c523160>"
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       "<table id=\"T_88cfc\">\n",
       "  <caption>Table A1b - Economists vs ChatGPT4o - Clark Center Claims</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_88cfc_level0_col0\" class=\"col_heading level0 col0\" colspan=\"6\">Economics Profs Survey</th>\n",
       "      <th id=\"T_88cfc_level0_col6\" class=\"col_heading level0 col6\" colspan=\"6\">ChatGPT4o</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_88cfc_level1_col0\" class=\"col_heading level1 col0\" >Strongly Agree</th>\n",
       "      <th id=\"T_88cfc_level1_col1\" class=\"col_heading level1 col1\" >Agree</th>\n",
       "      <th id=\"T_88cfc_level1_col2\" class=\"col_heading level1 col2\" >Disagree</th>\n",
       "      <th id=\"T_88cfc_level1_col3\" class=\"col_heading level1 col3\" >Strongly Disagree</th>\n",
       "      <th id=\"T_88cfc_level1_col4\" class=\"col_heading level1 col4\" >Uncertain</th>\n",
       "      <th id=\"T_88cfc_level1_col5\" class=\"col_heading level1 col5\" >No Opinion</th>\n",
       "      <th id=\"T_88cfc_level1_col6\" class=\"col_heading level1 col6\" >Strongly Agree</th>\n",
       "      <th id=\"T_88cfc_level1_col7\" class=\"col_heading level1 col7\" >Agree</th>\n",
       "      <th id=\"T_88cfc_level1_col8\" class=\"col_heading level1 col8\" >Disagree</th>\n",
       "      <th id=\"T_88cfc_level1_col9\" class=\"col_heading level1 col9\" >Strongly Disagree</th>\n",
       "      <th id=\"T_88cfc_level1_col10\" class=\"col_heading level1 col10\" >Uncertain</th>\n",
       "      <th id=\"T_88cfc_level1_col11\" class=\"col_heading level1 col11\" >No Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_88cfc_row0_col0\" class=\"data row0 col0\" >3.7</td>\n",
       "      <td id=\"T_88cfc_row0_col1\" class=\"data row0 col1\" >50.0</td>\n",
       "      <td id=\"T_88cfc_row0_col2\" class=\"data row0 col2\" >7.3</td>\n",
       "      <td id=\"T_88cfc_row0_col3\" class=\"data row0 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row0_col4\" class=\"data row0 col4\" >32.9</td>\n",
       "      <td id=\"T_88cfc_row0_col5\" class=\"data row0 col5\" >6.1</td>\n",
       "      <td id=\"T_88cfc_row0_col6\" class=\"data row0 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row0_col7\" class=\"data row0 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row0_col8\" class=\"data row0 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row0_col9\" class=\"data row0 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row0_col10\" class=\"data row0 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_88cfc_row1_col0\" class=\"data row1 col0\" >9.0</td>\n",
       "      <td id=\"T_88cfc_row1_col1\" class=\"data row1 col1\" >43.6</td>\n",
       "      <td id=\"T_88cfc_row1_col2\" class=\"data row1 col2\" >12.8</td>\n",
       "      <td id=\"T_88cfc_row1_col3\" class=\"data row1 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row1_col4\" class=\"data row1 col4\" >32.1</td>\n",
       "      <td id=\"T_88cfc_row1_col5\" class=\"data row1 col5\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row1_col6\" class=\"data row1 col6\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row1_col7\" class=\"data row1 col7\" >96.0</td>\n",
       "      <td id=\"T_88cfc_row1_col8\" class=\"data row1 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row1_col9\" class=\"data row1 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row1_col10\" class=\"data row1 col10\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row1_col11\" class=\"data row1 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_88cfc_row2_col0\" class=\"data row2 col0\" >9.3</td>\n",
       "      <td id=\"T_88cfc_row2_col1\" class=\"data row2 col1\" >44.0</td>\n",
       "      <td id=\"T_88cfc_row2_col2\" class=\"data row2 col2\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row2_col3\" class=\"data row2 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row2_col4\" class=\"data row2 col4\" >41.3</td>\n",
       "      <td id=\"T_88cfc_row2_col5\" class=\"data row2 col5\" >4.0</td>\n",
       "      <td id=\"T_88cfc_row2_col6\" class=\"data row2 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row2_col7\" class=\"data row2 col7\" >25.5</td>\n",
       "      <td id=\"T_88cfc_row2_col8\" class=\"data row2 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row2_col9\" class=\"data row2 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row2_col10\" class=\"data row2 col10\" >74.5</td>\n",
       "      <td id=\"T_88cfc_row2_col11\" class=\"data row2 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_88cfc_row3_col0\" class=\"data row3 col0\" >22.7</td>\n",
       "      <td id=\"T_88cfc_row3_col1\" class=\"data row3 col1\" >66.7</td>\n",
       "      <td id=\"T_88cfc_row3_col2\" class=\"data row3 col2\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row3_col3\" class=\"data row3 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row3_col4\" class=\"data row3 col4\" >5.3</td>\n",
       "      <td id=\"T_88cfc_row3_col5\" class=\"data row3 col5\" >4.0</td>\n",
       "      <td id=\"T_88cfc_row3_col6\" class=\"data row3 col6\" >2.0</td>\n",
       "      <td id=\"T_88cfc_row3_col7\" class=\"data row3 col7\" >98.0</td>\n",
       "      <td id=\"T_88cfc_row3_col8\" class=\"data row3 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row3_col9\" class=\"data row3 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row3_col10\" class=\"data row3 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_88cfc_row4_col0\" class=\"data row4 col0\" >18.2</td>\n",
       "      <td id=\"T_88cfc_row4_col1\" class=\"data row4 col1\" >63.6</td>\n",
       "      <td id=\"T_88cfc_row4_col2\" class=\"data row4 col2\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row4_col3\" class=\"data row4 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row4_col4\" class=\"data row4 col4\" >14.3</td>\n",
       "      <td id=\"T_88cfc_row4_col5\" class=\"data row4 col5\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row4_col6\" class=\"data row4 col6\" >46.5</td>\n",
       "      <td id=\"T_88cfc_row4_col7\" class=\"data row4 col7\" >53.5</td>\n",
       "      <td id=\"T_88cfc_row4_col8\" class=\"data row4 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row4_col9\" class=\"data row4 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row4_col10\" class=\"data row4 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_88cfc_row5_col0\" class=\"data row5 col0\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row5_col1\" class=\"data row5 col1\" >49.4</td>\n",
       "      <td id=\"T_88cfc_row5_col2\" class=\"data row5 col2\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row5_col3\" class=\"data row5 col3\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row5_col4\" class=\"data row5 col4\" >42.9</td>\n",
       "      <td id=\"T_88cfc_row5_col5\" class=\"data row5 col5\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row5_col6\" class=\"data row5 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row5_col7\" class=\"data row5 col7\" >90.5</td>\n",
       "      <td id=\"T_88cfc_row5_col8\" class=\"data row5 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row5_col9\" class=\"data row5 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row5_col10\" class=\"data row5 col10\" >9.5</td>\n",
       "      <td id=\"T_88cfc_row5_col11\" class=\"data row5 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_88cfc_row6_col0\" class=\"data row6 col0\" >6.5</td>\n",
       "      <td id=\"T_88cfc_row6_col1\" class=\"data row6 col1\" >51.9</td>\n",
       "      <td id=\"T_88cfc_row6_col2\" class=\"data row6 col2\" >7.8</td>\n",
       "      <td id=\"T_88cfc_row6_col3\" class=\"data row6 col3\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row6_col4\" class=\"data row6 col4\" >28.6</td>\n",
       "      <td id=\"T_88cfc_row6_col5\" class=\"data row6 col5\" >3.9</td>\n",
       "      <td id=\"T_88cfc_row6_col6\" class=\"data row6 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row6_col7\" class=\"data row6 col7\" >45.5</td>\n",
       "      <td id=\"T_88cfc_row6_col8\" class=\"data row6 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row6_col9\" class=\"data row6 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row6_col10\" class=\"data row6 col10\" >54.5</td>\n",
       "      <td id=\"T_88cfc_row6_col11\" class=\"data row6 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_88cfc_row7_col0\" class=\"data row7 col0\" >14.8</td>\n",
       "      <td id=\"T_88cfc_row7_col1\" class=\"data row7 col1\" >42.0</td>\n",
       "      <td id=\"T_88cfc_row7_col2\" class=\"data row7 col2\" >9.9</td>\n",
       "      <td id=\"T_88cfc_row7_col3\" class=\"data row7 col3\" >4.9</td>\n",
       "      <td id=\"T_88cfc_row7_col4\" class=\"data row7 col4\" >27.2</td>\n",
       "      <td id=\"T_88cfc_row7_col5\" class=\"data row7 col5\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row7_col6\" class=\"data row7 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row7_col7\" class=\"data row7 col7\" >98.5</td>\n",
       "      <td id=\"T_88cfc_row7_col8\" class=\"data row7 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row7_col9\" class=\"data row7 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row7_col10\" class=\"data row7 col10\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row7_col11\" class=\"data row7 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_88cfc_row8_col0\" class=\"data row8 col0\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row8_col1\" class=\"data row8 col1\" >22.2</td>\n",
       "      <td id=\"T_88cfc_row8_col2\" class=\"data row8 col2\" >31.9</td>\n",
       "      <td id=\"T_88cfc_row8_col3\" class=\"data row8 col3\" >2.8</td>\n",
       "      <td id=\"T_88cfc_row8_col4\" class=\"data row8 col4\" >27.8</td>\n",
       "      <td id=\"T_88cfc_row8_col5\" class=\"data row8 col5\" >13.9</td>\n",
       "      <td id=\"T_88cfc_row8_col6\" class=\"data row8 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row8_col7\" class=\"data row8 col7\" >38.0</td>\n",
       "      <td id=\"T_88cfc_row8_col8\" class=\"data row8 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row8_col9\" class=\"data row8 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row8_col10\" class=\"data row8 col10\" >62.0</td>\n",
       "      <td id=\"T_88cfc_row8_col11\" class=\"data row8 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_88cfc_row9_col0\" class=\"data row9 col0\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row9_col1\" class=\"data row9 col1\" >26.8</td>\n",
       "      <td id=\"T_88cfc_row9_col2\" class=\"data row9 col2\" >12.2</td>\n",
       "      <td id=\"T_88cfc_row9_col3\" class=\"data row9 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row9_col4\" class=\"data row9 col4\" >53.7</td>\n",
       "      <td id=\"T_88cfc_row9_col5\" class=\"data row9 col5\" >4.9</td>\n",
       "      <td id=\"T_88cfc_row9_col6\" class=\"data row9 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row9_col7\" class=\"data row9 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row9_col8\" class=\"data row9 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row9_col9\" class=\"data row9 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row9_col10\" class=\"data row9 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row9_col11\" class=\"data row9 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_88cfc_row10_col0\" class=\"data row10 col0\" >4.9</td>\n",
       "      <td id=\"T_88cfc_row10_col1\" class=\"data row10 col1\" >57.3</td>\n",
       "      <td id=\"T_88cfc_row10_col2\" class=\"data row10 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row10_col3\" class=\"data row10 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row10_col4\" class=\"data row10 col4\" >32.9</td>\n",
       "      <td id=\"T_88cfc_row10_col5\" class=\"data row10 col5\" >4.9</td>\n",
       "      <td id=\"T_88cfc_row10_col6\" class=\"data row10 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row10_col7\" class=\"data row10 col7\" >99.5</td>\n",
       "      <td id=\"T_88cfc_row10_col8\" class=\"data row10 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row10_col9\" class=\"data row10 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row10_col10\" class=\"data row10 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row10_col11\" class=\"data row10 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_88cfc_row11_col0\" class=\"data row11 col0\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row11_col1\" class=\"data row11 col1\" >25.6</td>\n",
       "      <td id=\"T_88cfc_row11_col2\" class=\"data row11 col2\" >14.6</td>\n",
       "      <td id=\"T_88cfc_row11_col3\" class=\"data row11 col3\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row11_col4\" class=\"data row11 col4\" >54.9</td>\n",
       "      <td id=\"T_88cfc_row11_col5\" class=\"data row11 col5\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row11_col6\" class=\"data row11 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row11_col7\" class=\"data row11 col7\" >16.0</td>\n",
       "      <td id=\"T_88cfc_row11_col8\" class=\"data row11 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row11_col9\" class=\"data row11 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row11_col10\" class=\"data row11 col10\" >84.0</td>\n",
       "      <td id=\"T_88cfc_row11_col11\" class=\"data row11 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_88cfc_row12_col0\" class=\"data row12 col0\" >11.7</td>\n",
       "      <td id=\"T_88cfc_row12_col1\" class=\"data row12 col1\" >54.5</td>\n",
       "      <td id=\"T_88cfc_row12_col2\" class=\"data row12 col2\" >6.5</td>\n",
       "      <td id=\"T_88cfc_row12_col3\" class=\"data row12 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row12_col4\" class=\"data row12 col4\" >26.0</td>\n",
       "      <td id=\"T_88cfc_row12_col5\" class=\"data row12 col5\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row12_col6\" class=\"data row12 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row12_col7\" class=\"data row12 col7\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row12_col8\" class=\"data row12 col8\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row12_col9\" class=\"data row12 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row12_col10\" class=\"data row12 col10\" >99.0</td>\n",
       "      <td id=\"T_88cfc_row12_col11\" class=\"data row12 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_88cfc_row13_col0\" class=\"data row13 col0\" >8.8</td>\n",
       "      <td id=\"T_88cfc_row13_col1\" class=\"data row13 col1\" >42.5</td>\n",
       "      <td id=\"T_88cfc_row13_col2\" class=\"data row13 col2\" >8.8</td>\n",
       "      <td id=\"T_88cfc_row13_col3\" class=\"data row13 col3\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row13_col4\" class=\"data row13 col4\" >33.8</td>\n",
       "      <td id=\"T_88cfc_row13_col5\" class=\"data row13 col5\" >5.0</td>\n",
       "      <td id=\"T_88cfc_row13_col6\" class=\"data row13 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row13_col7\" class=\"data row13 col7\" >7.0</td>\n",
       "      <td id=\"T_88cfc_row13_col8\" class=\"data row13 col8\" >82.0</td>\n",
       "      <td id=\"T_88cfc_row13_col9\" class=\"data row13 col9\" >10.5</td>\n",
       "      <td id=\"T_88cfc_row13_col10\" class=\"data row13 col10\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row13_col11\" class=\"data row13 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_88cfc_row14_col0\" class=\"data row14 col0\" >29.6</td>\n",
       "      <td id=\"T_88cfc_row14_col1\" class=\"data row14 col1\" >56.8</td>\n",
       "      <td id=\"T_88cfc_row14_col2\" class=\"data row14 col2\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row14_col3\" class=\"data row14 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row14_col4\" class=\"data row14 col4\" >4.9</td>\n",
       "      <td id=\"T_88cfc_row14_col5\" class=\"data row14 col5\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row14_col6\" class=\"data row14 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row14_col7\" class=\"data row14 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row14_col8\" class=\"data row14 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row14_col9\" class=\"data row14 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row14_col10\" class=\"data row14 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row14_col11\" class=\"data row14 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_88cfc_row15_col0\" class=\"data row15 col0\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row15_col1\" class=\"data row15 col1\" >22.0</td>\n",
       "      <td id=\"T_88cfc_row15_col2\" class=\"data row15 col2\" >15.9</td>\n",
       "      <td id=\"T_88cfc_row15_col3\" class=\"data row15 col3\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row15_col4\" class=\"data row15 col4\" >51.2</td>\n",
       "      <td id=\"T_88cfc_row15_col5\" class=\"data row15 col5\" >6.1</td>\n",
       "      <td id=\"T_88cfc_row15_col6\" class=\"data row15 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row15_col7\" class=\"data row15 col7\" >41.0</td>\n",
       "      <td id=\"T_88cfc_row15_col8\" class=\"data row15 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row15_col9\" class=\"data row15 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row15_col10\" class=\"data row15 col10\" >59.0</td>\n",
       "      <td id=\"T_88cfc_row15_col11\" class=\"data row15 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
       "      <td id=\"T_88cfc_row16_col0\" class=\"data row16 col0\" >38.7</td>\n",
       "      <td id=\"T_88cfc_row16_col1\" class=\"data row16 col1\" >50.7</td>\n",
       "      <td id=\"T_88cfc_row16_col2\" class=\"data row16 col2\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row16_col3\" class=\"data row16 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row16_col4\" class=\"data row16 col4\" >2.7</td>\n",
       "      <td id=\"T_88cfc_row16_col5\" class=\"data row16 col5\" >6.7</td>\n",
       "      <td id=\"T_88cfc_row16_col6\" class=\"data row16 col6\" >12.5</td>\n",
       "      <td id=\"T_88cfc_row16_col7\" class=\"data row16 col7\" >87.5</td>\n",
       "      <td id=\"T_88cfc_row16_col8\" class=\"data row16 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row16_col9\" class=\"data row16 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row16_col10\" class=\"data row16 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row16_col11\" class=\"data row16 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_88cfc_row17_col0\" class=\"data row17 col0\" >11.1</td>\n",
       "      <td id=\"T_88cfc_row17_col1\" class=\"data row17 col1\" >49.4</td>\n",
       "      <td id=\"T_88cfc_row17_col2\" class=\"data row17 col2\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row17_col3\" class=\"data row17 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row17_col4\" class=\"data row17 col4\" >34.6</td>\n",
       "      <td id=\"T_88cfc_row17_col5\" class=\"data row17 col5\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row17_col6\" class=\"data row17 col6\" >5.5</td>\n",
       "      <td id=\"T_88cfc_row17_col7\" class=\"data row17 col7\" >94.5</td>\n",
       "      <td id=\"T_88cfc_row17_col8\" class=\"data row17 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row17_col9\" class=\"data row17 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row17_col10\" class=\"data row17 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row17_col11\" class=\"data row17 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
       "      <td id=\"T_88cfc_row18_col0\" class=\"data row18 col0\" >9.7</td>\n",
       "      <td id=\"T_88cfc_row18_col1\" class=\"data row18 col1\" >40.3</td>\n",
       "      <td id=\"T_88cfc_row18_col2\" class=\"data row18 col2\" >2.8</td>\n",
       "      <td id=\"T_88cfc_row18_col3\" class=\"data row18 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row18_col4\" class=\"data row18 col4\" >33.3</td>\n",
       "      <td id=\"T_88cfc_row18_col5\" class=\"data row18 col5\" >13.9</td>\n",
       "      <td id=\"T_88cfc_row18_col6\" class=\"data row18 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row18_col7\" class=\"data row18 col7\" >95.5</td>\n",
       "      <td id=\"T_88cfc_row18_col8\" class=\"data row18 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row18_col9\" class=\"data row18 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row18_col10\" class=\"data row18 col10\" >4.5</td>\n",
       "      <td id=\"T_88cfc_row18_col11\" class=\"data row18 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_88cfc_row19_col0\" class=\"data row19 col0\" >23.8</td>\n",
       "      <td id=\"T_88cfc_row19_col1\" class=\"data row19 col1\" >57.1</td>\n",
       "      <td id=\"T_88cfc_row19_col2\" class=\"data row19 col2\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row19_col3\" class=\"data row19 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row19_col4\" class=\"data row19 col4\" >15.5</td>\n",
       "      <td id=\"T_88cfc_row19_col5\" class=\"data row19 col5\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row19_col6\" class=\"data row19 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row19_col7\" class=\"data row19 col7\" >96.5</td>\n",
       "      <td id=\"T_88cfc_row19_col8\" class=\"data row19 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row19_col9\" class=\"data row19 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row19_col10\" class=\"data row19 col10\" >3.0</td>\n",
       "      <td id=\"T_88cfc_row19_col11\" class=\"data row19 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "      <td id=\"T_88cfc_row20_col0\" class=\"data row20 col0\" >4.8</td>\n",
       "      <td id=\"T_88cfc_row20_col1\" class=\"data row20 col1\" >15.5</td>\n",
       "      <td id=\"T_88cfc_row20_col2\" class=\"data row20 col2\" >32.1</td>\n",
       "      <td id=\"T_88cfc_row20_col3\" class=\"data row20 col3\" >3.6</td>\n",
       "      <td id=\"T_88cfc_row20_col4\" class=\"data row20 col4\" >41.7</td>\n",
       "      <td id=\"T_88cfc_row20_col5\" class=\"data row20 col5\" >2.4</td>\n",
       "      <td id=\"T_88cfc_row20_col6\" class=\"data row20 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row20_col7\" class=\"data row20 col7\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row20_col8\" class=\"data row20 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row20_col9\" class=\"data row20 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row20_col10\" class=\"data row20 col10\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row20_col11\" class=\"data row20 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row21\" class=\"row_heading level0 row21\" >22</th>\n",
       "      <td id=\"T_88cfc_row21_col0\" class=\"data row21 col0\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row21_col1\" class=\"data row21 col1\" >35.9</td>\n",
       "      <td id=\"T_88cfc_row21_col2\" class=\"data row21 col2\" >16.7</td>\n",
       "      <td id=\"T_88cfc_row21_col3\" class=\"data row21 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row21_col4\" class=\"data row21 col4\" >42.3</td>\n",
       "      <td id=\"T_88cfc_row21_col5\" class=\"data row21 col5\" >2.6</td>\n",
       "      <td id=\"T_88cfc_row21_col6\" class=\"data row21 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row21_col7\" class=\"data row21 col7\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row21_col8\" class=\"data row21 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row21_col9\" class=\"data row21 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row21_col10\" class=\"data row21 col10\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row21_col11\" class=\"data row21 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row22\" class=\"row_heading level0 row22\" >23</th>\n",
       "      <td id=\"T_88cfc_row22_col0\" class=\"data row22 col0\" >24.7</td>\n",
       "      <td id=\"T_88cfc_row22_col1\" class=\"data row22 col1\" >63.0</td>\n",
       "      <td id=\"T_88cfc_row22_col2\" class=\"data row22 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row22_col3\" class=\"data row22 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row22_col4\" class=\"data row22 col4\" >11.1</td>\n",
       "      <td id=\"T_88cfc_row22_col5\" class=\"data row22 col5\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row22_col6\" class=\"data row22 col6\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row22_col7\" class=\"data row22 col7\" >91.5</td>\n",
       "      <td id=\"T_88cfc_row22_col8\" class=\"data row22 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row22_col9\" class=\"data row22 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row22_col10\" class=\"data row22 col10\" >7.0</td>\n",
       "      <td id=\"T_88cfc_row22_col11\" class=\"data row22 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row23\" class=\"row_heading level0 row23\" >24</th>\n",
       "      <td id=\"T_88cfc_row23_col0\" class=\"data row23 col0\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row23_col1\" class=\"data row23 col1\" >50.0</td>\n",
       "      <td id=\"T_88cfc_row23_col2\" class=\"data row23 col2\" >9.7</td>\n",
       "      <td id=\"T_88cfc_row23_col3\" class=\"data row23 col3\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row23_col4\" class=\"data row23 col4\" >25.0</td>\n",
       "      <td id=\"T_88cfc_row23_col5\" class=\"data row23 col5\" >12.5</td>\n",
       "      <td id=\"T_88cfc_row23_col6\" class=\"data row23 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row23_col7\" class=\"data row23 col7\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row23_col8\" class=\"data row23 col8\" >99.0</td>\n",
       "      <td id=\"T_88cfc_row23_col9\" class=\"data row23 col9\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row23_col10\" class=\"data row23 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row23_col11\" class=\"data row23 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row24\" class=\"row_heading level0 row24\" >25</th>\n",
       "      <td id=\"T_88cfc_row24_col0\" class=\"data row24 col0\" >63.8</td>\n",
       "      <td id=\"T_88cfc_row24_col1\" class=\"data row24 col1\" >32.5</td>\n",
       "      <td id=\"T_88cfc_row24_col2\" class=\"data row24 col2\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row24_col3\" class=\"data row24 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row24_col4\" class=\"data row24 col4\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row24_col5\" class=\"data row24 col5\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row24_col6\" class=\"data row24 col6\" >22.0</td>\n",
       "      <td id=\"T_88cfc_row24_col7\" class=\"data row24 col7\" >78.0</td>\n",
       "      <td id=\"T_88cfc_row24_col8\" class=\"data row24 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row24_col9\" class=\"data row24 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row24_col10\" class=\"data row24 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row24_col11\" class=\"data row24 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row25\" class=\"row_heading level0 row25\" >26</th>\n",
       "      <td id=\"T_88cfc_row25_col0\" class=\"data row25 col0\" >5.2</td>\n",
       "      <td id=\"T_88cfc_row25_col1\" class=\"data row25 col1\" >40.3</td>\n",
       "      <td id=\"T_88cfc_row25_col2\" class=\"data row25 col2\" >7.8</td>\n",
       "      <td id=\"T_88cfc_row25_col3\" class=\"data row25 col3\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row25_col4\" class=\"data row25 col4\" >39.0</td>\n",
       "      <td id=\"T_88cfc_row25_col5\" class=\"data row25 col5\" >6.5</td>\n",
       "      <td id=\"T_88cfc_row25_col6\" class=\"data row25 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row25_col7\" class=\"data row25 col7\" >26.0</td>\n",
       "      <td id=\"T_88cfc_row25_col8\" class=\"data row25 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row25_col9\" class=\"data row25 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row25_col10\" class=\"data row25 col10\" >74.0</td>\n",
       "      <td id=\"T_88cfc_row25_col11\" class=\"data row25 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row26\" class=\"row_heading level0 row26\" >27</th>\n",
       "      <td id=\"T_88cfc_row26_col0\" class=\"data row26 col0\" >47.5</td>\n",
       "      <td id=\"T_88cfc_row26_col1\" class=\"data row26 col1\" >46.2</td>\n",
       "      <td id=\"T_88cfc_row26_col2\" class=\"data row26 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col3\" class=\"data row26 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col4\" class=\"data row26 col4\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row26_col5\" class=\"data row26 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col6\" class=\"data row26 col6\" >56.0</td>\n",
       "      <td id=\"T_88cfc_row26_col7\" class=\"data row26 col7\" >44.0</td>\n",
       "      <td id=\"T_88cfc_row26_col8\" class=\"data row26 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col9\" class=\"data row26 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col10\" class=\"data row26 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row26_col11\" class=\"data row26 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row27\" class=\"row_heading level0 row27\" >28</th>\n",
       "      <td id=\"T_88cfc_row27_col0\" class=\"data row27 col0\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row27_col1\" class=\"data row27 col1\" >52.3</td>\n",
       "      <td id=\"T_88cfc_row27_col2\" class=\"data row27 col2\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row27_col3\" class=\"data row27 col3\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row27_col4\" class=\"data row27 col4\" >35.4</td>\n",
       "      <td id=\"T_88cfc_row27_col5\" class=\"data row27 col5\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row27_col6\" class=\"data row27 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row27_col7\" class=\"data row27 col7\" >99.5</td>\n",
       "      <td id=\"T_88cfc_row27_col8\" class=\"data row27 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row27_col9\" class=\"data row27 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row27_col10\" class=\"data row27 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row27_col11\" class=\"data row27 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row28\" class=\"row_heading level0 row28\" >29</th>\n",
       "      <td id=\"T_88cfc_row28_col0\" class=\"data row28 col0\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row28_col1\" class=\"data row28 col1\" >35.4</td>\n",
       "      <td id=\"T_88cfc_row28_col2\" class=\"data row28 col2\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row28_col3\" class=\"data row28 col3\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row28_col4\" class=\"data row28 col4\" >50.8</td>\n",
       "      <td id=\"T_88cfc_row28_col5\" class=\"data row28 col5\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row28_col6\" class=\"data row28 col6\" >10.5</td>\n",
       "      <td id=\"T_88cfc_row28_col7\" class=\"data row28 col7\" >89.5</td>\n",
       "      <td id=\"T_88cfc_row28_col8\" class=\"data row28 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row28_col9\" class=\"data row28 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row28_col10\" class=\"data row28 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row28_col11\" class=\"data row28 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row29\" class=\"row_heading level0 row29\" >30</th>\n",
       "      <td id=\"T_88cfc_row29_col0\" class=\"data row29 col0\" >37.7</td>\n",
       "      <td id=\"T_88cfc_row29_col1\" class=\"data row29 col1\" >61.0</td>\n",
       "      <td id=\"T_88cfc_row29_col2\" class=\"data row29 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col3\" class=\"data row29 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col4\" class=\"data row29 col4\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row29_col5\" class=\"data row29 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col6\" class=\"data row29 col6\" >88.5</td>\n",
       "      <td id=\"T_88cfc_row29_col7\" class=\"data row29 col7\" >11.5</td>\n",
       "      <td id=\"T_88cfc_row29_col8\" class=\"data row29 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col9\" class=\"data row29 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col10\" class=\"data row29 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row29_col11\" class=\"data row29 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row30\" class=\"row_heading level0 row30\" >31</th>\n",
       "      <td id=\"T_88cfc_row30_col0\" class=\"data row30 col0\" >2.9</td>\n",
       "      <td id=\"T_88cfc_row30_col1\" class=\"data row30 col1\" >36.8</td>\n",
       "      <td id=\"T_88cfc_row30_col2\" class=\"data row30 col2\" >10.3</td>\n",
       "      <td id=\"T_88cfc_row30_col3\" class=\"data row30 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row30_col4\" class=\"data row30 col4\" >33.8</td>\n",
       "      <td id=\"T_88cfc_row30_col5\" class=\"data row30 col5\" >16.2</td>\n",
       "      <td id=\"T_88cfc_row30_col6\" class=\"data row30 col6\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row30_col7\" class=\"data row30 col7\" >53.0</td>\n",
       "      <td id=\"T_88cfc_row30_col8\" class=\"data row30 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row30_col9\" class=\"data row30 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row30_col10\" class=\"data row30 col10\" >46.0</td>\n",
       "      <td id=\"T_88cfc_row30_col11\" class=\"data row30 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row31\" class=\"row_heading level0 row31\" >32</th>\n",
       "      <td id=\"T_88cfc_row31_col0\" class=\"data row31 col0\" >6.8</td>\n",
       "      <td id=\"T_88cfc_row31_col1\" class=\"data row31 col1\" >30.1</td>\n",
       "      <td id=\"T_88cfc_row31_col2\" class=\"data row31 col2\" >31.5</td>\n",
       "      <td id=\"T_88cfc_row31_col3\" class=\"data row31 col3\" >4.1</td>\n",
       "      <td id=\"T_88cfc_row31_col4\" class=\"data row31 col4\" >23.3</td>\n",
       "      <td id=\"T_88cfc_row31_col5\" class=\"data row31 col5\" >4.1</td>\n",
       "      <td id=\"T_88cfc_row31_col6\" class=\"data row31 col6\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row31_col7\" class=\"data row31 col7\" >22.5</td>\n",
       "      <td id=\"T_88cfc_row31_col8\" class=\"data row31 col8\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row31_col9\" class=\"data row31 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row31_col10\" class=\"data row31 col10\" >74.0</td>\n",
       "      <td id=\"T_88cfc_row31_col11\" class=\"data row31 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row32\" class=\"row_heading level0 row32\" >33</th>\n",
       "      <td id=\"T_88cfc_row32_col0\" class=\"data row32 col0\" >13.0</td>\n",
       "      <td id=\"T_88cfc_row32_col1\" class=\"data row32 col1\" >40.6</td>\n",
       "      <td id=\"T_88cfc_row32_col2\" class=\"data row32 col2\" >17.4</td>\n",
       "      <td id=\"T_88cfc_row32_col3\" class=\"data row32 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row32_col4\" class=\"data row32 col4\" >23.2</td>\n",
       "      <td id=\"T_88cfc_row32_col5\" class=\"data row32 col5\" >5.8</td>\n",
       "      <td id=\"T_88cfc_row32_col6\" class=\"data row32 col6\" >6.0</td>\n",
       "      <td id=\"T_88cfc_row32_col7\" class=\"data row32 col7\" >94.0</td>\n",
       "      <td id=\"T_88cfc_row32_col8\" class=\"data row32 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row32_col9\" class=\"data row32 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row32_col10\" class=\"data row32 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row32_col11\" class=\"data row32 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row33\" class=\"row_heading level0 row33\" >34</th>\n",
       "      <td id=\"T_88cfc_row33_col0\" class=\"data row33 col0\" >13.7</td>\n",
       "      <td id=\"T_88cfc_row33_col1\" class=\"data row33 col1\" >43.8</td>\n",
       "      <td id=\"T_88cfc_row33_col2\" class=\"data row33 col2\" >15.1</td>\n",
       "      <td id=\"T_88cfc_row33_col3\" class=\"data row33 col3\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row33_col4\" class=\"data row33 col4\" >21.9</td>\n",
       "      <td id=\"T_88cfc_row33_col5\" class=\"data row33 col5\" >4.1</td>\n",
       "      <td id=\"T_88cfc_row33_col6\" class=\"data row33 col6\" >6.0</td>\n",
       "      <td id=\"T_88cfc_row33_col7\" class=\"data row33 col7\" >94.0</td>\n",
       "      <td id=\"T_88cfc_row33_col8\" class=\"data row33 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row33_col9\" class=\"data row33 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row33_col10\" class=\"data row33 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row33_col11\" class=\"data row33 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row34\" class=\"row_heading level0 row34\" >35</th>\n",
       "      <td id=\"T_88cfc_row34_col0\" class=\"data row34 col0\" >13.0</td>\n",
       "      <td id=\"T_88cfc_row34_col1\" class=\"data row34 col1\" >70.1</td>\n",
       "      <td id=\"T_88cfc_row34_col2\" class=\"data row34 col2\" >3.9</td>\n",
       "      <td id=\"T_88cfc_row34_col3\" class=\"data row34 col3\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row34_col4\" class=\"data row34 col4\" >11.7</td>\n",
       "      <td id=\"T_88cfc_row34_col5\" class=\"data row34 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row34_col6\" class=\"data row34 col6\" >10.0</td>\n",
       "      <td id=\"T_88cfc_row34_col7\" class=\"data row34 col7\" >90.0</td>\n",
       "      <td id=\"T_88cfc_row34_col8\" class=\"data row34 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row34_col9\" class=\"data row34 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row34_col10\" class=\"data row34 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row34_col11\" class=\"data row34 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row35\" class=\"row_heading level0 row35\" >36</th>\n",
       "      <td id=\"T_88cfc_row35_col0\" class=\"data row35 col0\" >7.1</td>\n",
       "      <td id=\"T_88cfc_row35_col1\" class=\"data row35 col1\" >31.4</td>\n",
       "      <td id=\"T_88cfc_row35_col2\" class=\"data row35 col2\" >14.3</td>\n",
       "      <td id=\"T_88cfc_row35_col3\" class=\"data row35 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row35_col4\" class=\"data row35 col4\" >38.6</td>\n",
       "      <td id=\"T_88cfc_row35_col5\" class=\"data row35 col5\" >8.6</td>\n",
       "      <td id=\"T_88cfc_row35_col6\" class=\"data row35 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row35_col7\" class=\"data row35 col7\" >7.5</td>\n",
       "      <td id=\"T_88cfc_row35_col8\" class=\"data row35 col8\" >8.0</td>\n",
       "      <td id=\"T_88cfc_row35_col9\" class=\"data row35 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row35_col10\" class=\"data row35 col10\" >82.5</td>\n",
       "      <td id=\"T_88cfc_row35_col11\" class=\"data row35 col11\" >2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row36\" class=\"row_heading level0 row36\" >37</th>\n",
       "      <td id=\"T_88cfc_row36_col0\" class=\"data row36 col0\" >58.8</td>\n",
       "      <td id=\"T_88cfc_row36_col1\" class=\"data row36 col1\" >36.2</td>\n",
       "      <td id=\"T_88cfc_row36_col2\" class=\"data row36 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row36_col3\" class=\"data row36 col3\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row36_col4\" class=\"data row36 col4\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row36_col5\" class=\"data row36 col5\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row36_col6\" class=\"data row36 col6\" >26.0</td>\n",
       "      <td id=\"T_88cfc_row36_col7\" class=\"data row36 col7\" >74.0</td>\n",
       "      <td id=\"T_88cfc_row36_col8\" class=\"data row36 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row36_col9\" class=\"data row36 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row36_col10\" class=\"data row36 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row36_col11\" class=\"data row36 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row37\" class=\"row_heading level0 row37\" >38</th>\n",
       "      <td id=\"T_88cfc_row37_col0\" class=\"data row37 col0\" >11.4</td>\n",
       "      <td id=\"T_88cfc_row37_col1\" class=\"data row37 col1\" >65.7</td>\n",
       "      <td id=\"T_88cfc_row37_col2\" class=\"data row37 col2\" >5.7</td>\n",
       "      <td id=\"T_88cfc_row37_col3\" class=\"data row37 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row37_col4\" class=\"data row37 col4\" >10.0</td>\n",
       "      <td id=\"T_88cfc_row37_col5\" class=\"data row37 col5\" >7.1</td>\n",
       "      <td id=\"T_88cfc_row37_col6\" class=\"data row37 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row37_col7\" class=\"data row37 col7\" >38.0</td>\n",
       "      <td id=\"T_88cfc_row37_col8\" class=\"data row37 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row37_col9\" class=\"data row37 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row37_col10\" class=\"data row37 col10\" >61.5</td>\n",
       "      <td id=\"T_88cfc_row37_col11\" class=\"data row37 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row38\" class=\"row_heading level0 row38\" >39</th>\n",
       "      <td id=\"T_88cfc_row38_col0\" class=\"data row38 col0\" >5.5</td>\n",
       "      <td id=\"T_88cfc_row38_col1\" class=\"data row38 col1\" >30.1</td>\n",
       "      <td id=\"T_88cfc_row38_col2\" class=\"data row38 col2\" >15.1</td>\n",
       "      <td id=\"T_88cfc_row38_col3\" class=\"data row38 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row38_col4\" class=\"data row38 col4\" >45.2</td>\n",
       "      <td id=\"T_88cfc_row38_col5\" class=\"data row38 col5\" >4.1</td>\n",
       "      <td id=\"T_88cfc_row38_col6\" class=\"data row38 col6\" >8.5</td>\n",
       "      <td id=\"T_88cfc_row38_col7\" class=\"data row38 col7\" >74.5</td>\n",
       "      <td id=\"T_88cfc_row38_col8\" class=\"data row38 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row38_col9\" class=\"data row38 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row38_col10\" class=\"data row38 col10\" >17.0</td>\n",
       "      <td id=\"T_88cfc_row38_col11\" class=\"data row38 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row39\" class=\"row_heading level0 row39\" >40</th>\n",
       "      <td id=\"T_88cfc_row39_col0\" class=\"data row39 col0\" >10.0</td>\n",
       "      <td id=\"T_88cfc_row39_col1\" class=\"data row39 col1\" >57.1</td>\n",
       "      <td id=\"T_88cfc_row39_col2\" class=\"data row39 col2\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row39_col3\" class=\"data row39 col3\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row39_col4\" class=\"data row39 col4\" >22.9</td>\n",
       "      <td id=\"T_88cfc_row39_col5\" class=\"data row39 col5\" >7.1</td>\n",
       "      <td id=\"T_88cfc_row39_col6\" class=\"data row39 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row39_col7\" class=\"data row39 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row39_col8\" class=\"data row39 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row39_col9\" class=\"data row39 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row39_col10\" class=\"data row39 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row39_col11\" class=\"data row39 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row40\" class=\"row_heading level0 row40\" >41</th>\n",
       "      <td id=\"T_88cfc_row40_col0\" class=\"data row40 col0\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row40_col1\" class=\"data row40 col1\" >27.8</td>\n",
       "      <td id=\"T_88cfc_row40_col2\" class=\"data row40 col2\" >16.7</td>\n",
       "      <td id=\"T_88cfc_row40_col3\" class=\"data row40 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row40_col4\" class=\"data row40 col4\" >43.1</td>\n",
       "      <td id=\"T_88cfc_row40_col5\" class=\"data row40 col5\" >11.1</td>\n",
       "      <td id=\"T_88cfc_row40_col6\" class=\"data row40 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row40_col7\" class=\"data row40 col7\" >98.5</td>\n",
       "      <td id=\"T_88cfc_row40_col8\" class=\"data row40 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row40_col9\" class=\"data row40 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row40_col10\" class=\"data row40 col10\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row40_col11\" class=\"data row40 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row41\" class=\"row_heading level0 row41\" >42</th>\n",
       "      <td id=\"T_88cfc_row41_col0\" class=\"data row41 col0\" >2.9</td>\n",
       "      <td id=\"T_88cfc_row41_col1\" class=\"data row41 col1\" >46.4</td>\n",
       "      <td id=\"T_88cfc_row41_col2\" class=\"data row41 col2\" >7.2</td>\n",
       "      <td id=\"T_88cfc_row41_col3\" class=\"data row41 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row41_col4\" class=\"data row41 col4\" >27.5</td>\n",
       "      <td id=\"T_88cfc_row41_col5\" class=\"data row41 col5\" >15.9</td>\n",
       "      <td id=\"T_88cfc_row41_col6\" class=\"data row41 col6\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row41_col7\" class=\"data row41 col7\" >62.5</td>\n",
       "      <td id=\"T_88cfc_row41_col8\" class=\"data row41 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row41_col9\" class=\"data row41 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row41_col10\" class=\"data row41 col10\" >36.0</td>\n",
       "      <td id=\"T_88cfc_row41_col11\" class=\"data row41 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row42\" class=\"row_heading level0 row42\" >43</th>\n",
       "      <td id=\"T_88cfc_row42_col0\" class=\"data row42 col0\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row42_col1\" class=\"data row42 col1\" >13.0</td>\n",
       "      <td id=\"T_88cfc_row42_col2\" class=\"data row42 col2\" >20.3</td>\n",
       "      <td id=\"T_88cfc_row42_col3\" class=\"data row42 col3\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row42_col4\" class=\"data row42 col4\" >47.8</td>\n",
       "      <td id=\"T_88cfc_row42_col5\" class=\"data row42 col5\" >17.4</td>\n",
       "      <td id=\"T_88cfc_row42_col6\" class=\"data row42 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row42_col7\" class=\"data row42 col7\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row42_col8\" class=\"data row42 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row42_col9\" class=\"data row42 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row42_col10\" class=\"data row42 col10\" >99.0</td>\n",
       "      <td id=\"T_88cfc_row42_col11\" class=\"data row42 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row43\" class=\"row_heading level0 row43\" >44</th>\n",
       "      <td id=\"T_88cfc_row43_col0\" class=\"data row43 col0\" >25.0</td>\n",
       "      <td id=\"T_88cfc_row43_col1\" class=\"data row43 col1\" >59.7</td>\n",
       "      <td id=\"T_88cfc_row43_col2\" class=\"data row43 col2\" >2.8</td>\n",
       "      <td id=\"T_88cfc_row43_col3\" class=\"data row43 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row43_col4\" class=\"data row43 col4\" >11.1</td>\n",
       "      <td id=\"T_88cfc_row43_col5\" class=\"data row43 col5\" >1.4</td>\n",
       "      <td id=\"T_88cfc_row43_col6\" class=\"data row43 col6\" >59.5</td>\n",
       "      <td id=\"T_88cfc_row43_col7\" class=\"data row43 col7\" >40.5</td>\n",
       "      <td id=\"T_88cfc_row43_col8\" class=\"data row43 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row43_col9\" class=\"data row43 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row43_col10\" class=\"data row43 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row43_col11\" class=\"data row43 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row44\" class=\"row_heading level0 row44\" >45</th>\n",
       "      <td id=\"T_88cfc_row44_col0\" class=\"data row44 col0\" >2.5</td>\n",
       "      <td id=\"T_88cfc_row44_col1\" class=\"data row44 col1\" >38.8</td>\n",
       "      <td id=\"T_88cfc_row44_col2\" class=\"data row44 col2\" >23.8</td>\n",
       "      <td id=\"T_88cfc_row44_col3\" class=\"data row44 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row44_col4\" class=\"data row44 col4\" >28.8</td>\n",
       "      <td id=\"T_88cfc_row44_col5\" class=\"data row44 col5\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row44_col6\" class=\"data row44 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row44_col7\" class=\"data row44 col7\" >27.0</td>\n",
       "      <td id=\"T_88cfc_row44_col8\" class=\"data row44 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row44_col9\" class=\"data row44 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row44_col10\" class=\"data row44 col10\" >73.0</td>\n",
       "      <td id=\"T_88cfc_row44_col11\" class=\"data row44 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row45\" class=\"row_heading level0 row45\" >46</th>\n",
       "      <td id=\"T_88cfc_row45_col0\" class=\"data row45 col0\" >9.7</td>\n",
       "      <td id=\"T_88cfc_row45_col1\" class=\"data row45 col1\" >54.2</td>\n",
       "      <td id=\"T_88cfc_row45_col2\" class=\"data row45 col2\" >4.2</td>\n",
       "      <td id=\"T_88cfc_row45_col3\" class=\"data row45 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row45_col4\" class=\"data row45 col4\" >29.2</td>\n",
       "      <td id=\"T_88cfc_row45_col5\" class=\"data row45 col5\" >2.8</td>\n",
       "      <td id=\"T_88cfc_row45_col6\" class=\"data row45 col6\" >20.0</td>\n",
       "      <td id=\"T_88cfc_row45_col7\" class=\"data row45 col7\" >80.0</td>\n",
       "      <td id=\"T_88cfc_row45_col8\" class=\"data row45 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row45_col9\" class=\"data row45 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row45_col10\" class=\"data row45 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row45_col11\" class=\"data row45 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row46\" class=\"row_heading level0 row46\" >47</th>\n",
       "      <td id=\"T_88cfc_row46_col0\" class=\"data row46 col0\" >25.0</td>\n",
       "      <td id=\"T_88cfc_row46_col1\" class=\"data row46 col1\" >64.7</td>\n",
       "      <td id=\"T_88cfc_row46_col2\" class=\"data row46 col2\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row46_col3\" class=\"data row46 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row46_col4\" class=\"data row46 col4\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row46_col5\" class=\"data row46 col5\" >8.8</td>\n",
       "      <td id=\"T_88cfc_row46_col6\" class=\"data row46 col6\" >6.0</td>\n",
       "      <td id=\"T_88cfc_row46_col7\" class=\"data row46 col7\" >94.0</td>\n",
       "      <td id=\"T_88cfc_row46_col8\" class=\"data row46 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row46_col9\" class=\"data row46 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row46_col10\" class=\"data row46 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row46_col11\" class=\"data row46 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row47\" class=\"row_heading level0 row47\" >48</th>\n",
       "      <td id=\"T_88cfc_row47_col0\" class=\"data row47 col0\" >11.7</td>\n",
       "      <td id=\"T_88cfc_row47_col1\" class=\"data row47 col1\" >59.7</td>\n",
       "      <td id=\"T_88cfc_row47_col2\" class=\"data row47 col2\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row47_col3\" class=\"data row47 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col4\" class=\"data row47 col4\" >27.3</td>\n",
       "      <td id=\"T_88cfc_row47_col5\" class=\"data row47 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col6\" class=\"data row47 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col7\" class=\"data row47 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row47_col8\" class=\"data row47 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col9\" class=\"data row47 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col10\" class=\"data row47 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row47_col11\" class=\"data row47 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row48\" class=\"row_heading level0 row48\" >49</th>\n",
       "      <td id=\"T_88cfc_row48_col0\" class=\"data row48 col0\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col1\" class=\"data row48 col1\" >40.5</td>\n",
       "      <td id=\"T_88cfc_row48_col2\" class=\"data row48 col2\" >11.9</td>\n",
       "      <td id=\"T_88cfc_row48_col3\" class=\"data row48 col3\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row48_col4\" class=\"data row48 col4\" >46.4</td>\n",
       "      <td id=\"T_88cfc_row48_col5\" class=\"data row48 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col6\" class=\"data row48 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col7\" class=\"data row48 col7\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col8\" class=\"data row48 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col9\" class=\"data row48 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row48_col10\" class=\"data row48 col10\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row48_col11\" class=\"data row48 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row49\" class=\"row_heading level0 row49\" >50</th>\n",
       "      <td id=\"T_88cfc_row49_col0\" class=\"data row49 col0\" >10.7</td>\n",
       "      <td id=\"T_88cfc_row49_col1\" class=\"data row49 col1\" >59.5</td>\n",
       "      <td id=\"T_88cfc_row49_col2\" class=\"data row49 col2\" >4.8</td>\n",
       "      <td id=\"T_88cfc_row49_col3\" class=\"data row49 col3\" >1.2</td>\n",
       "      <td id=\"T_88cfc_row49_col4\" class=\"data row49 col4\" >23.8</td>\n",
       "      <td id=\"T_88cfc_row49_col5\" class=\"data row49 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row49_col6\" class=\"data row49 col6\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row49_col7\" class=\"data row49 col7\" >95.0</td>\n",
       "      <td id=\"T_88cfc_row49_col8\" class=\"data row49 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row49_col9\" class=\"data row49 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row49_col10\" class=\"data row49 col10\" >4.0</td>\n",
       "      <td id=\"T_88cfc_row49_col11\" class=\"data row49 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row50\" class=\"row_heading level0 row50\" >51</th>\n",
       "      <td id=\"T_88cfc_row50_col0\" class=\"data row50 col0\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row50_col1\" class=\"data row50 col1\" >30.8</td>\n",
       "      <td id=\"T_88cfc_row50_col2\" class=\"data row50 col2\" >24.6</td>\n",
       "      <td id=\"T_88cfc_row50_col3\" class=\"data row50 col3\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row50_col4\" class=\"data row50 col4\" >32.3</td>\n",
       "      <td id=\"T_88cfc_row50_col5\" class=\"data row50 col5\" >9.2</td>\n",
       "      <td id=\"T_88cfc_row50_col6\" class=\"data row50 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row50_col7\" class=\"data row50 col7\" >99.0</td>\n",
       "      <td id=\"T_88cfc_row50_col8\" class=\"data row50 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row50_col9\" class=\"data row50 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row50_col10\" class=\"data row50 col10\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row50_col11\" class=\"data row50 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row51\" class=\"row_heading level0 row51\" >52</th>\n",
       "      <td id=\"T_88cfc_row51_col0\" class=\"data row51 col0\" >12.3</td>\n",
       "      <td id=\"T_88cfc_row51_col1\" class=\"data row51 col1\" >56.9</td>\n",
       "      <td id=\"T_88cfc_row51_col2\" class=\"data row51 col2\" >4.6</td>\n",
       "      <td id=\"T_88cfc_row51_col3\" class=\"data row51 col3\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row51_col4\" class=\"data row51 col4\" >18.5</td>\n",
       "      <td id=\"T_88cfc_row51_col5\" class=\"data row51 col5\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row51_col6\" class=\"data row51 col6\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row51_col7\" class=\"data row51 col7\" >99.0</td>\n",
       "      <td id=\"T_88cfc_row51_col8\" class=\"data row51 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row51_col9\" class=\"data row51 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row51_col10\" class=\"data row51 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row51_col11\" class=\"data row51 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row52\" class=\"row_heading level0 row52\" >53</th>\n",
       "      <td id=\"T_88cfc_row52_col0\" class=\"data row52 col0\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row52_col1\" class=\"data row52 col1\" >33.8</td>\n",
       "      <td id=\"T_88cfc_row52_col2\" class=\"data row52 col2\" >33.8</td>\n",
       "      <td id=\"T_88cfc_row52_col3\" class=\"data row52 col3\" >4.6</td>\n",
       "      <td id=\"T_88cfc_row52_col4\" class=\"data row52 col4\" >20.0</td>\n",
       "      <td id=\"T_88cfc_row52_col5\" class=\"data row52 col5\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row52_col6\" class=\"data row52 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row52_col7\" class=\"data row52 col7\" >6.5</td>\n",
       "      <td id=\"T_88cfc_row52_col8\" class=\"data row52 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row52_col9\" class=\"data row52 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row52_col10\" class=\"data row52 col10\" >93.5</td>\n",
       "      <td id=\"T_88cfc_row52_col11\" class=\"data row52 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row53\" class=\"row_heading level0 row53\" >54</th>\n",
       "      <td id=\"T_88cfc_row53_col0\" class=\"data row53 col0\" >7.7</td>\n",
       "      <td id=\"T_88cfc_row53_col1\" class=\"data row53 col1\" >35.4</td>\n",
       "      <td id=\"T_88cfc_row53_col2\" class=\"data row53 col2\" >7.7</td>\n",
       "      <td id=\"T_88cfc_row53_col3\" class=\"data row53 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row53_col4\" class=\"data row53 col4\" >44.6</td>\n",
       "      <td id=\"T_88cfc_row53_col5\" class=\"data row53 col5\" >4.6</td>\n",
       "      <td id=\"T_88cfc_row53_col6\" class=\"data row53 col6\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row53_col7\" class=\"data row53 col7\" >99.5</td>\n",
       "      <td id=\"T_88cfc_row53_col8\" class=\"data row53 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row53_col9\" class=\"data row53 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row53_col10\" class=\"data row53 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row53_col11\" class=\"data row53 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row54\" class=\"row_heading level0 row54\" >55</th>\n",
       "      <td id=\"T_88cfc_row54_col0\" class=\"data row54 col0\" >6.2</td>\n",
       "      <td id=\"T_88cfc_row54_col1\" class=\"data row54 col1\" >30.8</td>\n",
       "      <td id=\"T_88cfc_row54_col2\" class=\"data row54 col2\" >12.3</td>\n",
       "      <td id=\"T_88cfc_row54_col3\" class=\"data row54 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row54_col4\" class=\"data row54 col4\" >49.2</td>\n",
       "      <td id=\"T_88cfc_row54_col5\" class=\"data row54 col5\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row54_col6\" class=\"data row54 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row54_col7\" class=\"data row54 col7\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row54_col8\" class=\"data row54 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row54_col9\" class=\"data row54 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row54_col10\" class=\"data row54 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row54_col11\" class=\"data row54 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row55\" class=\"row_heading level0 row55\" >56</th>\n",
       "      <td id=\"T_88cfc_row55_col0\" class=\"data row55 col0\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row55_col1\" class=\"data row55 col1\" >43.1</td>\n",
       "      <td id=\"T_88cfc_row55_col2\" class=\"data row55 col2\" >7.7</td>\n",
       "      <td id=\"T_88cfc_row55_col3\" class=\"data row55 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col4\" class=\"data row55 col4\" >46.2</td>\n",
       "      <td id=\"T_88cfc_row55_col5\" class=\"data row55 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col6\" class=\"data row55 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col7\" class=\"data row55 col7\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col8\" class=\"data row55 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col9\" class=\"data row55 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row55_col10\" class=\"data row55 col10\" >100.0</td>\n",
       "      <td id=\"T_88cfc_row55_col11\" class=\"data row55 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row56\" class=\"row_heading level0 row56\" >57</th>\n",
       "      <td id=\"T_88cfc_row56_col0\" class=\"data row56 col0\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col1\" class=\"data row56 col1\" >16.9</td>\n",
       "      <td id=\"T_88cfc_row56_col2\" class=\"data row56 col2\" >10.4</td>\n",
       "      <td id=\"T_88cfc_row56_col3\" class=\"data row56 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col4\" class=\"data row56 col4\" >72.7</td>\n",
       "      <td id=\"T_88cfc_row56_col5\" class=\"data row56 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col6\" class=\"data row56 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col7\" class=\"data row56 col7\" >12.0</td>\n",
       "      <td id=\"T_88cfc_row56_col8\" class=\"data row56 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col9\" class=\"data row56 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row56_col10\" class=\"data row56 col10\" >88.0</td>\n",
       "      <td id=\"T_88cfc_row56_col11\" class=\"data row56 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row57\" class=\"row_heading level0 row57\" >58</th>\n",
       "      <td id=\"T_88cfc_row57_col0\" class=\"data row57 col0\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row57_col1\" class=\"data row57 col1\" >44.2</td>\n",
       "      <td id=\"T_88cfc_row57_col2\" class=\"data row57 col2\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row57_col3\" class=\"data row57 col3\" >1.3</td>\n",
       "      <td id=\"T_88cfc_row57_col4\" class=\"data row57 col4\" >51.9</td>\n",
       "      <td id=\"T_88cfc_row57_col5\" class=\"data row57 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row57_col6\" class=\"data row57 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row57_col7\" class=\"data row57 col7\" >21.5</td>\n",
       "      <td id=\"T_88cfc_row57_col8\" class=\"data row57 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row57_col9\" class=\"data row57 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row57_col10\" class=\"data row57 col10\" >78.5</td>\n",
       "      <td id=\"T_88cfc_row57_col11\" class=\"data row57 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row58\" class=\"row_heading level0 row58\" >59</th>\n",
       "      <td id=\"T_88cfc_row58_col0\" class=\"data row58 col0\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row58_col1\" class=\"data row58 col1\" >46.2</td>\n",
       "      <td id=\"T_88cfc_row58_col2\" class=\"data row58 col2\" >27.7</td>\n",
       "      <td id=\"T_88cfc_row58_col3\" class=\"data row58 col3\" >3.1</td>\n",
       "      <td id=\"T_88cfc_row58_col4\" class=\"data row58 col4\" >20.0</td>\n",
       "      <td id=\"T_88cfc_row58_col5\" class=\"data row58 col5\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row58_col6\" class=\"data row58 col6\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row58_col7\" class=\"data row58 col7\" >17.5</td>\n",
       "      <td id=\"T_88cfc_row58_col8\" class=\"data row58 col8\" >1.0</td>\n",
       "      <td id=\"T_88cfc_row58_col9\" class=\"data row58 col9\" >0.5</td>\n",
       "      <td id=\"T_88cfc_row58_col10\" class=\"data row58 col10\" >81.0</td>\n",
       "      <td id=\"T_88cfc_row58_col11\" class=\"data row58 col11\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_88cfc_level0_row59\" class=\"row_heading level0 row59\" >60</th>\n",
       "      <td id=\"T_88cfc_row59_col0\" class=\"data row59 col0\" >18.1</td>\n",
       "      <td id=\"T_88cfc_row59_col1\" class=\"data row59 col1\" >37.5</td>\n",
       "      <td id=\"T_88cfc_row59_col2\" class=\"data row59 col2\" >15.3</td>\n",
       "      <td id=\"T_88cfc_row59_col3\" class=\"data row59 col3\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row59_col4\" class=\"data row59 col4\" >26.4</td>\n",
       "      <td id=\"T_88cfc_row59_col5\" class=\"data row59 col5\" >2.8</td>\n",
       "      <td id=\"T_88cfc_row59_col6\" class=\"data row59 col6\" >1.5</td>\n",
       "      <td id=\"T_88cfc_row59_col7\" class=\"data row59 col7\" >98.5</td>\n",
       "      <td id=\"T_88cfc_row59_col8\" class=\"data row59 col8\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row59_col9\" class=\"data row59 col9\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row59_col10\" class=\"data row59 col10\" >0.0</td>\n",
       "      <td id=\"T_88cfc_row59_col11\" class=\"data row59 col11\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd5da2de20>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# table A1 annex\n",
    "#a=GPT4overview['Claim']\n",
    "#b=togetheropinion['Claim']\n",
    "#np.sum(a==b)\n",
    "\n",
    "c=togetheropinion['Claim']\n",
    "c=c.rename(\" \")\n",
    "c=c.to_frame()\n",
    "c.index = np.arange(1, len(c) + 1)\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'},\n",
    "        {'selector': '.true2', 'props': 'border-right:solid; border-color:blue; vertical-align:top'},\n",
    "         {'selector': '.true3', 'props': 'color: blue;border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "display(c.style.set_caption(\"Table A1a - Clark Center Claims\").set_table_styles(styles))\n",
    "        #.set_properties(**{'width': '500px'}))\n",
    "\n",
    "\n",
    "b=togetheropinion[['Strongly Agree', 'Agree', 'Disagree','Strongly Disagree', 'Uncertain', 'No Opinion']]\n",
    "a=GPT4overview[['Strongly Agree Chatgpt', 'Agree Chatgpt', 'Disagree Chatgpt', 'Strongly Disagree Chatgpt', 'Uncertain Chatgpt', 'No Opinion Chatgpt']]\n",
    "a.columns=b.columns\n",
    "b=pd.concat([b], keys=['Economics Profs Survey'], axis=1)\n",
    "a=pd.concat([a], keys=['ChatGPT4o'], axis=1)\n",
    "a.index = np.arange(1, len(a) + 1)\n",
    "b.index = np.arange(1, len(b) + 1)\n",
    "\n",
    "pd.concat([b,a], axis=1).style.set_caption(\"Table A1b - Economists vs ChatGPT4o - Clark Center Claims\").set_table_styles(styles).format(precision=1)\n",
    "\n",
    "#byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 4o'], axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054abbca-189c-40fd-b8ae-0d621d120f76",
   "metadata": {},
   "source": [
    "# Part B: Sievertsen and Smith, 2023 survey statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "c44d1190-e98a-4de5-8c99-80b4a0c56a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Use of artificial intelligence over the next ten years will lead to a substantial increase in the growth rates of real per capita income in the US and Western Europe over the subsequent two decades.',\n",
       " 'There needs to be more government regulation around Twitter’s content moderation and personal data protection.',\n",
       " 'It would serve the US economy well to make it unlawful for companies with revenues over $1 billion to offer goods or services for sale at an excessive price during an exceptional market shock.',\n",
       " 'Efforts to achieve the goal of reaching net-zero emissions of greenhouse gases by 2050 will be a major drag on global economic growth.',\n",
       " 'Given the centrality of semiconductors to the manufacturing of many products, securing reliable supplies should be a key strategic objective of national policy.',\n",
       " 'A significant factor behind today’s higher US inflation is dominant corporations in uncompetitive markets taking advantage of their market power to raise prices.',\n",
       " 'Financial regulators in the US and Europe lack the tools and authority to deter runs on banks by uninsured depositors.',\n",
       " 'When economic policy-makers are unable to commit credibly in advance to a specific decision rule, they will often follow a poor policy trajectory.',\n",
       " 'A windfall tax on the profits of large oil companies‚ with the revenue rebated to households‚ would provide an efficient means to protect the average US household.',\n",
       " 'A ban on advertising junk foods (those that are high in sugar, salt, and fat) would be an effective policy to reduce child obesity.']"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# claims Sievertsen and Smith, 2023\n",
    "# these are the 10 claims of the https://www.economicsobservatory.com/how-do-the-views-of-experts-and-the-public-differ-on-big-policy-questions\n",
    "claimsorder10=['Use of artificial intelligence over the next ten years will lead to a substantial increase in the growth rates of real per capita income in the US and Western Europe over the subsequent two decades.'\n",
    ",'There needs to be more government regulation around Twitter’s content moderation and personal data protection.'\n",
    ",'It would serve the US economy well to make it unlawful for companies with revenues over $1 billion to offer goods or services for sale at an excessive price during an exceptional market shock.'\n",
    ",'Efforts to achieve the goal of reaching net-zero emissions of greenhouse gases by 2050 will be a major drag on global economic growth.'\n",
    ",'Given the centrality of semiconductors to the manufacturing of many products, securing reliable supplies should be a key strategic objective of national policy.'\n",
    ",'A significant factor behind today’s higher US inflation is dominant corporations in uncompetitive markets taking advantage of their market power to raise prices.'\n",
    ",'Financial regulators in the US and Europe lack the tools and authority to deter runs on banks by uninsured depositors.'\n",
    ",'When economic policy-makers are unable to commit credibly in advance to a specific decision rule, they will often follow a poor policy trajectory.'\n",
    ",'A windfall tax on the profits of large oil companies‚ with the revenue rebated to households‚ would provide an efficient means to protect the average US household.'\n",
    ",'A ban on advertising junk foods (those that are high in sugar, salt, and fat) would be an effective policy to reduce child obesity.']\n",
    "claimsorder10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd45f29d-e13c-42d8-b080-3cbcc27b39d7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "\n",
    "n=200\n",
    "\n",
    "claimtexts=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "claimquestionanswergpt35=[]\n",
    "\n",
    "for j in range(0,len(claimsorder10)):\n",
    "    print(j)\n",
    "    claimtext=claimsorder10[j]\n",
    "    claimtexts+=[claimtext]\n",
    "    for i in range(0,n):\n",
    "        print(i)\n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "\n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: strongly disagree, disagree, uncertain, agree, strongly agree, no opinion. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        \n",
    "            \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "df['claimtext']=np.repeat(claimtexts,n)\n",
    "df.to_pickle('claimssievertsen090824')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "872ee0b6-d0be-4bcb-9c4d-1e4ee17d7824",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claimquestionanswergpt4oclean\n",
      "agree                1077\n",
      "uncertain             539\n",
      "strongly agree        197\n",
      "disagree              181\n",
      "no opinion              5\n",
      "strongly disagree       1\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4oprofclean\n",
      "uncertain            1006\n",
      "agree                 551\n",
      "strongly agree        239\n",
      "disagree              195\n",
      "strongly disagree       6\n",
      "no opinion              2\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt35clean\n",
      "strongly agree       823\n",
      "agree                757\n",
      "strongly disagree    347\n",
      "disagree              50\n",
      "uncertain             23\n",
      "Name: count, dtype: int64\n",
      "                   claimquestionanswergpt4o1  claimquestionanswergpt4oprof1  \\\n",
      "strongly agree                           NaN                            NaN   \n",
      "agree                                   26.5                            2.5   \n",
      "uncertain                               73.5                           97.5   \n",
      "disagree                                 NaN                            NaN   \n",
      "strongly disagree                        NaN                            NaN   \n",
      "no opinion                               NaN                            NaN   \n",
      "\n",
      "                   claimquestionanswergpt351  claimquestionanswergpt4o2  \\\n",
      "strongly agree                          64.0                        1.0   \n",
      "agree                                   35.5                       98.5   \n",
      "uncertain                                0.5                        NaN   \n",
      "disagree                                 NaN                        NaN   \n",
      "strongly disagree                        NaN                        NaN   \n",
      "no opinion                               NaN                        0.5   \n",
      "\n",
      "                   claimquestionanswergpt4oprof2  claimquestionanswergpt352  \\\n",
      "strongly agree                               NaN                       53.0   \n",
      "agree                                       87.0                       43.0   \n",
      "uncertain                                   12.0                        2.5   \n",
      "disagree                                     NaN                        0.5   \n",
      "strongly disagree                            NaN                        1.0   \n",
      "no opinion                                   1.0                        NaN   \n",
      "\n",
      "                   claimquestionanswergpt4o3  claimquestionanswergpt4oprof3  \\\n",
      "strongly agree                           1.0                            NaN   \n",
      "agree                                   68.5                            0.5   \n",
      "uncertain                               29.5                           93.0   \n",
      "disagree                                 NaN                            6.5   \n",
      "strongly disagree                        NaN                            NaN   \n",
      "no opinion                               1.0                            NaN   \n",
      "\n",
      "                   claimquestionanswergpt353  claimquestionanswergpt4o4  ...  \\\n",
      "strongly agree                           6.0                        NaN  ...   \n",
      "agree                                   28.5                        NaN  ...   \n",
      "uncertain                                3.5                       10.5  ...   \n",
      "disagree                                 7.0                       89.0  ...   \n",
      "strongly disagree                       55.0                        0.5  ...   \n",
      "no opinion                               NaN                        NaN  ...   \n",
      "\n",
      "                   claimquestionanswergpt357  claimquestionanswergpt4o8  \\\n",
      "strongly agree                          30.5                        2.0   \n",
      "agree                                   33.0                       98.0   \n",
      "uncertain                                2.0                        NaN   \n",
      "disagree                                 5.5                        NaN   \n",
      "strongly disagree                       29.0                        NaN   \n",
      "no opinion                               NaN                        NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof8  claimquestionanswergpt358  \\\n",
      "strongly agree                              20.5                       54.5   \n",
      "agree                                       79.5                       45.0   \n",
      "uncertain                                    NaN                        NaN   \n",
      "disagree                                     NaN                        NaN   \n",
      "strongly disagree                            NaN                        0.5   \n",
      "no opinion                                   NaN                        NaN   \n",
      "\n",
      "                   claimquestionanswergpt4o9  claimquestionanswergpt4oprof9  \\\n",
      "strongly agree                           0.5                            NaN   \n",
      "agree                                   88.0                           50.5   \n",
      "uncertain                               10.5                           49.0   \n",
      "disagree                                 NaN                            NaN   \n",
      "strongly disagree                        NaN                            NaN   \n",
      "no opinion                               1.0                            NaN   \n",
      "\n",
      "                   claimquestionanswergpt359  claimquestionanswergpt4o10  \\\n",
      "strongly agree                          24.5                         NaN   \n",
      "agree                                   74.0                       100.0   \n",
      "uncertain                                1.0                         NaN   \n",
      "disagree                                 0.5                         NaN   \n",
      "strongly disagree                        NaN                         NaN   \n",
      "no opinion                               NaN                         NaN   \n",
      "\n",
      "                   claimquestionanswergpt4oprof10  claimquestionanswergpt3510  \n",
      "strongly agree                                NaN                        66.0  \n",
      "agree                                        36.5                        32.5  \n",
      "uncertain                                    63.5                         0.5  \n",
      "disagree                                      NaN                         0.5  \n",
      "strongly disagree                             NaN                         0.5  \n",
      "no opinion                                    NaN                         NaN  \n",
      "\n",
      "[6 rows x 30 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_pickle('claimssievertsen090824')\n",
    "n=200\n",
    "df['claimquestionanswergpt4oclean']=None\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('agree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean']=None\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('agree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oprofclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt35clean']=None\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('no opinion', case=False)]='no opinion'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('disagree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('agree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt35clean'].value_counts())\n",
    "\n",
    "\n",
    "\n",
    "# overview by question\n",
    "fixedform=pd.DataFrame(index=['strongly agree','agree', 'uncertain', 'disagree', 'strongly disagree','no opinion'])\n",
    "ct=0\n",
    "for i in claimsorder10:\n",
    "    ct=ct+1\n",
    "    dfs=df[df['claimtext']==i]    \n",
    "    fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
    "\n",
    "print(fixedform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "7645c571-d63c-4763-91c9-522dfdff4be1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EconProf Opinion</th>\n",
       "      <th>EconProf Agree</th>\n",
       "      <th>Public Opinion</th>\n",
       "      <th>Public Agree</th>\n",
       "      <th>ChatGPT35 Opinion</th>\n",
       "      <th>ChatGPT35 Agree</th>\n",
       "      <th>ChatGPT4o Opinion</th>\n",
       "      <th>ChatGPT4o Agree</th>\n",
       "      <th>ChatGPT4oProf Opinion</th>\n",
       "      <th>ChatGPT4oProf Agree</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>46.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>26.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Twitter</th>\n",
       "      <td>53.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price Gouging</th>\n",
       "      <td>70.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>35.8</td>\n",
       "      <td>69.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net-zero</th>\n",
       "      <td>47.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>89.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Semi-conductors</th>\n",
       "      <td>76.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greedflation</th>\n",
       "      <td>74.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>27.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Financial Regulators</th>\n",
       "      <td>61.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>64.8</td>\n",
       "      <td>27.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>36.5</td>\n",
       "      <td>47.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Economic Policy</th>\n",
       "      <td>63.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Windfall Tax</th>\n",
       "      <td>65.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>88.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Junk Foods</th>\n",
       "      <td>53.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>36.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      EconProf Opinion  EconProf Agree  Public Opinion  \\\n",
       "Name                                                                     \n",
       "AI                                46.0            96.0            43.0   \n",
       "Twitter                           53.0            70.0            51.0   \n",
       "Price Gouging                     70.0             7.0            40.0   \n",
       "Net-zero                          47.0            26.0            51.0   \n",
       "Semi-conductors                   76.0           100.0            41.0   \n",
       "Greedflation                      74.0            12.0            50.0   \n",
       "Financial Regulators              61.0            44.0            43.0   \n",
       "Economic Policy                   63.0            92.0            48.0   \n",
       "Windfall Tax                      65.0            54.0            43.0   \n",
       "Junk Foods                        53.0            83.0            54.0   \n",
       "\n",
       "                      Public Agree  ChatGPT35 Opinion  ChatGPT35 Agree  \\\n",
       "Name                                                                     \n",
       "AI                            51.0               99.5            100.0   \n",
       "Twitter                       65.0               97.5             98.5   \n",
       "Price Gouging                 80.0               96.5             35.8   \n",
       "Net-zero                      35.0               98.5              0.5   \n",
       "Semi-conductors               95.0              100.0            100.0   \n",
       "Greedflation                  66.0              100.0             99.5   \n",
       "Financial Regulators          56.0               98.0             64.8   \n",
       "Economic Policy               92.0              100.0             99.5   \n",
       "Windfall Tax                  77.0               99.0             99.5   \n",
       "Junk Foods                    61.0               99.5             99.0   \n",
       "\n",
       "                      ChatGPT4o Opinion  ChatGPT4o Agree  \\\n",
       "Name                                                       \n",
       "AI                                 26.5            100.0   \n",
       "Twitter                            99.5            100.0   \n",
       "Price Gouging                      69.5            100.0   \n",
       "Net-zero                           89.5              0.0   \n",
       "Semi-conductors                   100.0            100.0   \n",
       "Greedflation                       27.0            100.0   \n",
       "Financial Regulators               27.5             94.5   \n",
       "Economic Policy                   100.0            100.0   \n",
       "Windfall Tax                       88.5            100.0   \n",
       "Junk Foods                        100.0            100.0   \n",
       "\n",
       "                      ChatGPT4oProf Opinion  ChatGPT4oProf Agree  \n",
       "Name                                                              \n",
       "AI                                      2.5                100.0  \n",
       "Twitter                                87.0                100.0  \n",
       "Price Gouging                           7.0                  7.1  \n",
       "Net-zero                               75.0                  0.0  \n",
       "Semi-conductors                       100.0                100.0  \n",
       "Greedflation                            0.5                100.0  \n",
       "Financial Regulators                   36.5                 47.9  \n",
       "Economic Policy                       100.0                100.0  \n",
       "Windfall Tax                           51.0                100.0  \n",
       "Junk Foods                             36.5                100.0  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# outcome of 10 claims\n",
    "expertopinion = [0.46,0.53,0.7,0.47,0.76,0.74,0.61,0.63,0.65,0.53]\n",
    "expertagree=[0.96,0.7,0.07,0.26,1,0.12,0.44,0.92,0.54,0.83]\n",
    "publicopinion=[0.43,0.51,0.4,0.51,0.41,0.5,0.43,0.48,0.43,0.54]\n",
    "publicagree=[0.51,0.65,0.8,0.35,0.95,0.66,0.56,0.92,0.77,0.61]\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "claimform10=pd.DataFrame()\n",
    "claimform10['EconProf Opinion']=expertopinion\n",
    "claimform10['EconProf Agree']=expertagree\n",
    "\n",
    "claimform10['Public Opinion']=publicopinion\n",
    "claimform10['Public Agree']=publicagree\n",
    "claimform10=100*claimform10\n",
    "# overview by 10 claimsquestion\n",
    "for i in range(1,11):\n",
    "    a=[fixedform['claimquestionanswergpt35'+str(i)].at['uncertain'],fixedform['claimquestionanswergpt35'+str(i)].at['no opinion']]\n",
    "    claimform10.at[i-1,'ChatGPT35 Opinion']= 100-np.nansum(a)\n",
    "    a=[fixedform['claimquestionanswergpt35'+str(i)].at['strongly agree'],fixedform['claimquestionanswergpt35'+str(i)].at['agree']]\n",
    "    b=[fixedform['claimquestionanswergpt35'+str(i)].at['strongly disagree'],fixedform['claimquestionanswergpt35'+str(i)].at['disagree']]\n",
    "    claimform10.at[i-1,'ChatGPT35 Agree']= round(100*np.nansum(a)/(np.nansum(a)+np.nansum(b)),1)\n",
    "\n",
    "    \n",
    "    a=[fixedform['claimquestionanswergpt4o'+str(i)].at['uncertain'],fixedform['claimquestionanswergpt4o'+str(i)].at['no opinion']]\n",
    "    claimform10.at[i-1,'ChatGPT4o Opinion']= 100-np.nansum(a)\n",
    "    a=[fixedform['claimquestionanswergpt4o'+str(i)].at['strongly agree'],fixedform['claimquestionanswergpt4o'+str(i)].at['agree']]\n",
    "    b=[fixedform['claimquestionanswergpt4o'+str(i)].at['strongly disagree'],fixedform['claimquestionanswergpt4o'+str(i)].at['disagree']]\n",
    "    claimform10.at[i-1,'ChatGPT4o Agree']= round(100*np.nansum(a)/(np.nansum(a)+np.nansum(b)),1)\n",
    "\n",
    "    a=[fixedform['claimquestionanswergpt4oprof'+str(i)].at['uncertain'],fixedform['claimquestionanswergpt4oprof'+str(i)].at['no opinion']]\n",
    "    claimform10.at[i-1,'ChatGPT4oProf Opinion']= 100-np.nansum(a)\n",
    "    a=[fixedform['claimquestionanswergpt4oprof'+str(i)].at['strongly agree'],fixedform['claimquestionanswergpt4oprof'+str(i)].at['agree']]\n",
    "    b=[fixedform['claimquestionanswergpt4oprof'+str(i)].at['strongly disagree'],fixedform['claimquestionanswergpt4oprof'+str(i)].at['disagree']]\n",
    "    claimform10.at[i-1,'ChatGPT4oProf Agree']= round(100*np.nansum(a)/(np.nansum(a)+np.nansum(b)),1)\n",
    "\n",
    "\n",
    "\n",
    "claimform10['Name']=None\n",
    "claimform10.at[0,'Name']='AI'\n",
    "claimform10.at[1,'Name']='Twitter'\n",
    "claimform10.at[2,'Name']='Price Gouging'\n",
    "claimform10.at[3,'Name']='Net-zero'\n",
    "claimform10.at[4,'Name']='Semi-conductors'\n",
    "claimform10.at[5,'Name']='Greedflation'\n",
    "claimform10.at[6,'Name']='Financial Regulators'\n",
    "claimform10.at[7,'Name']='Economic Policy'\n",
    "claimform10.at[8,'Name']='Windfall Tax'\n",
    "claimform10.at[9,'Name']='Junk Foods'\n",
    "claimform10=claimform10.set_index('Name', drop=True)\n",
    "claimform10\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "c255ae72-2868-4aaa-8d92-d362226b01d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_3fb68 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_3fb68 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_3fb68 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_3fb68 .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_3fb68\">\n",
       "  <caption>Table 4 - % Expressing an Opinion</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_3fb68_level0_col0\" class=\"col_heading level0 col0\" >EconProf Opinion</th>\n",
       "      <th id=\"T_3fb68_level0_col1\" class=\"col_heading level0 col1\" >Public Opinion</th>\n",
       "      <th id=\"T_3fb68_level0_col2\" class=\"col_heading level0 col2\" >ChatGPT35 Opinion</th>\n",
       "      <th id=\"T_3fb68_level0_col3\" class=\"col_heading level0 col3\" >ChatGPT4o Opinion</th>\n",
       "      <th id=\"T_3fb68_level0_col4\" class=\"col_heading level0 col4\" >ChatGPT4oProf Opinion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row0\" class=\"row_heading level0 row0\" >AI</th>\n",
       "      <td id=\"T_3fb68_row0_col0\" class=\"data row0 col0\" >46.0</td>\n",
       "      <td id=\"T_3fb68_row0_col1\" class=\"data row0 col1\" >43.0</td>\n",
       "      <td id=\"T_3fb68_row0_col2\" class=\"data row0 col2\" >99.5</td>\n",
       "      <td id=\"T_3fb68_row0_col3\" class=\"data row0 col3\" >26.5</td>\n",
       "      <td id=\"T_3fb68_row0_col4\" class=\"data row0 col4\" >2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row1\" class=\"row_heading level0 row1\" >Twitter</th>\n",
       "      <td id=\"T_3fb68_row1_col0\" class=\"data row1 col0\" >53.0</td>\n",
       "      <td id=\"T_3fb68_row1_col1\" class=\"data row1 col1\" >51.0</td>\n",
       "      <td id=\"T_3fb68_row1_col2\" class=\"data row1 col2\" >97.5</td>\n",
       "      <td id=\"T_3fb68_row1_col3\" class=\"data row1 col3\" >99.5</td>\n",
       "      <td id=\"T_3fb68_row1_col4\" class=\"data row1 col4\" >87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row2\" class=\"row_heading level0 row2\" >Price Gouging</th>\n",
       "      <td id=\"T_3fb68_row2_col0\" class=\"data row2 col0\" >70.0</td>\n",
       "      <td id=\"T_3fb68_row2_col1\" class=\"data row2 col1\" >40.0</td>\n",
       "      <td id=\"T_3fb68_row2_col2\" class=\"data row2 col2\" >96.5</td>\n",
       "      <td id=\"T_3fb68_row2_col3\" class=\"data row2 col3\" >69.5</td>\n",
       "      <td id=\"T_3fb68_row2_col4\" class=\"data row2 col4\" >7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row3\" class=\"row_heading level0 row3\" >Net-zero</th>\n",
       "      <td id=\"T_3fb68_row3_col0\" class=\"data row3 col0\" >47.0</td>\n",
       "      <td id=\"T_3fb68_row3_col1\" class=\"data row3 col1\" >51.0</td>\n",
       "      <td id=\"T_3fb68_row3_col2\" class=\"data row3 col2\" >98.5</td>\n",
       "      <td id=\"T_3fb68_row3_col3\" class=\"data row3 col3\" >89.5</td>\n",
       "      <td id=\"T_3fb68_row3_col4\" class=\"data row3 col4\" >75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row4\" class=\"row_heading level0 row4\" >Semi-conductors</th>\n",
       "      <td id=\"T_3fb68_row4_col0\" class=\"data row4 col0\" >76.0</td>\n",
       "      <td id=\"T_3fb68_row4_col1\" class=\"data row4 col1\" >41.0</td>\n",
       "      <td id=\"T_3fb68_row4_col2\" class=\"data row4 col2\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row4_col3\" class=\"data row4 col3\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row4_col4\" class=\"data row4 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row5\" class=\"row_heading level0 row5\" >Greedflation</th>\n",
       "      <td id=\"T_3fb68_row5_col0\" class=\"data row5 col0\" >74.0</td>\n",
       "      <td id=\"T_3fb68_row5_col1\" class=\"data row5 col1\" >50.0</td>\n",
       "      <td id=\"T_3fb68_row5_col2\" class=\"data row5 col2\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row5_col3\" class=\"data row5 col3\" >27.0</td>\n",
       "      <td id=\"T_3fb68_row5_col4\" class=\"data row5 col4\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row6\" class=\"row_heading level0 row6\" >Financial Regulators</th>\n",
       "      <td id=\"T_3fb68_row6_col0\" class=\"data row6 col0\" >61.0</td>\n",
       "      <td id=\"T_3fb68_row6_col1\" class=\"data row6 col1\" >43.0</td>\n",
       "      <td id=\"T_3fb68_row6_col2\" class=\"data row6 col2\" >98.0</td>\n",
       "      <td id=\"T_3fb68_row6_col3\" class=\"data row6 col3\" >27.5</td>\n",
       "      <td id=\"T_3fb68_row6_col4\" class=\"data row6 col4\" >36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row7\" class=\"row_heading level0 row7\" >Economic Policy</th>\n",
       "      <td id=\"T_3fb68_row7_col0\" class=\"data row7 col0\" >63.0</td>\n",
       "      <td id=\"T_3fb68_row7_col1\" class=\"data row7 col1\" >48.0</td>\n",
       "      <td id=\"T_3fb68_row7_col2\" class=\"data row7 col2\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row7_col3\" class=\"data row7 col3\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row7_col4\" class=\"data row7 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row8\" class=\"row_heading level0 row8\" >Windfall Tax</th>\n",
       "      <td id=\"T_3fb68_row8_col0\" class=\"data row8 col0\" >65.0</td>\n",
       "      <td id=\"T_3fb68_row8_col1\" class=\"data row8 col1\" >43.0</td>\n",
       "      <td id=\"T_3fb68_row8_col2\" class=\"data row8 col2\" >99.0</td>\n",
       "      <td id=\"T_3fb68_row8_col3\" class=\"data row8 col3\" >88.5</td>\n",
       "      <td id=\"T_3fb68_row8_col4\" class=\"data row8 col4\" >51.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row9\" class=\"row_heading level0 row9\" >Junk Foods</th>\n",
       "      <td id=\"T_3fb68_row9_col0\" class=\"data row9 col0\" >53.0</td>\n",
       "      <td id=\"T_3fb68_row9_col1\" class=\"data row9 col1\" >54.0</td>\n",
       "      <td id=\"T_3fb68_row9_col2\" class=\"data row9 col2\" >99.5</td>\n",
       "      <td id=\"T_3fb68_row9_col3\" class=\"data row9 col3\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row9_col4\" class=\"data row9 col4\" >36.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row10\" class=\"row_heading level0 row10\" >mean</th>\n",
       "      <td id=\"T_3fb68_row10_col0\" class=\"data row10 col0\" >60.8</td>\n",
       "      <td id=\"T_3fb68_row10_col1\" class=\"data row10 col1\" >46.4</td>\n",
       "      <td id=\"T_3fb68_row10_col2\" class=\"data row10 col2\" >98.8</td>\n",
       "      <td id=\"T_3fb68_row10_col3\" class=\"data row10 col3\" >72.8</td>\n",
       "      <td id=\"T_3fb68_row10_col4\" class=\"data row10 col4\" >49.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row11\" class=\"row_heading level0 row11\" >min</th>\n",
       "      <td id=\"T_3fb68_row11_col0\" class=\"data row11 col0\" >46.0</td>\n",
       "      <td id=\"T_3fb68_row11_col1\" class=\"data row11 col1\" >40.0</td>\n",
       "      <td id=\"T_3fb68_row11_col2\" class=\"data row11 col2\" >96.5</td>\n",
       "      <td id=\"T_3fb68_row11_col3\" class=\"data row11 col3\" >26.5</td>\n",
       "      <td id=\"T_3fb68_row11_col4\" class=\"data row11 col4\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row12\" class=\"row_heading level0 row12\" >25%</th>\n",
       "      <td id=\"T_3fb68_row12_col0\" class=\"data row12 col0\" >53.0</td>\n",
       "      <td id=\"T_3fb68_row12_col1\" class=\"data row12 col1\" >43.0</td>\n",
       "      <td id=\"T_3fb68_row12_col2\" class=\"data row12 col2\" >98.1</td>\n",
       "      <td id=\"T_3fb68_row12_col3\" class=\"data row12 col3\" >38.0</td>\n",
       "      <td id=\"T_3fb68_row12_col4\" class=\"data row12 col4\" >14.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row13\" class=\"row_heading level0 row13\" >50%</th>\n",
       "      <td id=\"T_3fb68_row13_col0\" class=\"data row13 col0\" >62.0</td>\n",
       "      <td id=\"T_3fb68_row13_col1\" class=\"data row13 col1\" >45.5</td>\n",
       "      <td id=\"T_3fb68_row13_col2\" class=\"data row13 col2\" >99.2</td>\n",
       "      <td id=\"T_3fb68_row13_col3\" class=\"data row13 col3\" >89.0</td>\n",
       "      <td id=\"T_3fb68_row13_col4\" class=\"data row13 col4\" >43.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row14\" class=\"row_heading level0 row14\" >75%</th>\n",
       "      <td id=\"T_3fb68_row14_col0\" class=\"data row14 col0\" >68.8</td>\n",
       "      <td id=\"T_3fb68_row14_col1\" class=\"data row14 col1\" >50.8</td>\n",
       "      <td id=\"T_3fb68_row14_col2\" class=\"data row14 col2\" >99.9</td>\n",
       "      <td id=\"T_3fb68_row14_col3\" class=\"data row14 col3\" >99.9</td>\n",
       "      <td id=\"T_3fb68_row14_col4\" class=\"data row14 col4\" >84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_3fb68_level0_row15\" class=\"row_heading level0 row15\" >max</th>\n",
       "      <td id=\"T_3fb68_row15_col0\" class=\"data row15 col0\" >76.0</td>\n",
       "      <td id=\"T_3fb68_row15_col1\" class=\"data row15 col1\" >54.0</td>\n",
       "      <td id=\"T_3fb68_row15_col2\" class=\"data row15 col2\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row15_col3\" class=\"data row15 col3\" >100.0</td>\n",
       "      <td id=\"T_3fb68_row15_col4\" class=\"data row15 col4\" >100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd9c475e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on the 10 Clark Center Survey questions used by Sievertsen and Smith (2023). \n",
      "The data gives, for each question, the share of answers that expresses an opinion, followed by the descriptive statistics accross questions.\n",
      "The first column comes from a survey of Economics Profs while the second column comes from a representative survey of 100 people. \n",
      "The third to fifth column come from repeatedly querying different versions of ChatGPT \n"
     ]
    }
   ],
   "source": [
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "display(pd.concat([claimform10.filter(like='Opinion', axis=1), claimform10.filter(like='Opinion', axis=1).describe().loc[['mean','min','25%','50%', '75%','max']]], axis=0).style.format(precision=1).set_caption(\"Table 4 - % Expressing an Opinion\").set_table_styles(styles))\n",
    "print('Notes: this table is based on the 10 Clark Center Survey questions used by Sievertsen and Smith (2023). \\nThe data gives, for each question, the share of answers that expresses an opinion, followed by the descriptive statistics accross questions.\\nThe first column comes from a survey of Economics Profs while the second column comes from a representative survey of 100 people. \\nThe third to fifth column come from repeatedly querying different versions of ChatGPT ')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a8bb796a-d66b-49cb-8f8b-2fd6e6c6b6a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_30917 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_30917 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_30917 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_30917 .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_30917\">\n",
       "  <caption>Table 5 - % Agree, Out of Those Expressing an Opinion</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_30917_level0_col0\" class=\"col_heading level0 col0\" >EconProf Agree</th>\n",
       "      <th id=\"T_30917_level0_col1\" class=\"col_heading level0 col1\" >Public Agree</th>\n",
       "      <th id=\"T_30917_level0_col2\" class=\"col_heading level0 col2\" >ChatGPT35 Agree</th>\n",
       "      <th id=\"T_30917_level0_col3\" class=\"col_heading level0 col3\" >ChatGPT4o Agree</th>\n",
       "      <th id=\"T_30917_level0_col4\" class=\"col_heading level0 col4\" >ChatGPT4oProf Agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row0\" class=\"row_heading level0 row0\" >AI</th>\n",
       "      <td id=\"T_30917_row0_col0\" class=\"data row0 col0\" >96.0</td>\n",
       "      <td id=\"T_30917_row0_col1\" class=\"data row0 col1\" >51.0</td>\n",
       "      <td id=\"T_30917_row0_col2\" class=\"data row0 col2\" >100.0</td>\n",
       "      <td id=\"T_30917_row0_col3\" class=\"data row0 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row0_col4\" class=\"data row0 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row1\" class=\"row_heading level0 row1\" >Twitter</th>\n",
       "      <td id=\"T_30917_row1_col0\" class=\"data row1 col0\" >70.0</td>\n",
       "      <td id=\"T_30917_row1_col1\" class=\"data row1 col1\" >65.0</td>\n",
       "      <td id=\"T_30917_row1_col2\" class=\"data row1 col2\" >98.5</td>\n",
       "      <td id=\"T_30917_row1_col3\" class=\"data row1 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row1_col4\" class=\"data row1 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row2\" class=\"row_heading level0 row2\" >Price Gouging</th>\n",
       "      <td id=\"T_30917_row2_col0\" class=\"data row2 col0\" >7.0</td>\n",
       "      <td id=\"T_30917_row2_col1\" class=\"data row2 col1\" >80.0</td>\n",
       "      <td id=\"T_30917_row2_col2\" class=\"data row2 col2\" >35.8</td>\n",
       "      <td id=\"T_30917_row2_col3\" class=\"data row2 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row2_col4\" class=\"data row2 col4\" >7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row3\" class=\"row_heading level0 row3\" >Net-zero</th>\n",
       "      <td id=\"T_30917_row3_col0\" class=\"data row3 col0\" >26.0</td>\n",
       "      <td id=\"T_30917_row3_col1\" class=\"data row3 col1\" >35.0</td>\n",
       "      <td id=\"T_30917_row3_col2\" class=\"data row3 col2\" >0.5</td>\n",
       "      <td id=\"T_30917_row3_col3\" class=\"data row3 col3\" >0.0</td>\n",
       "      <td id=\"T_30917_row3_col4\" class=\"data row3 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row4\" class=\"row_heading level0 row4\" >Semi-conductors</th>\n",
       "      <td id=\"T_30917_row4_col0\" class=\"data row4 col0\" >100.0</td>\n",
       "      <td id=\"T_30917_row4_col1\" class=\"data row4 col1\" >95.0</td>\n",
       "      <td id=\"T_30917_row4_col2\" class=\"data row4 col2\" >100.0</td>\n",
       "      <td id=\"T_30917_row4_col3\" class=\"data row4 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row4_col4\" class=\"data row4 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row5\" class=\"row_heading level0 row5\" >Greedflation</th>\n",
       "      <td id=\"T_30917_row5_col0\" class=\"data row5 col0\" >12.0</td>\n",
       "      <td id=\"T_30917_row5_col1\" class=\"data row5 col1\" >66.0</td>\n",
       "      <td id=\"T_30917_row5_col2\" class=\"data row5 col2\" >99.5</td>\n",
       "      <td id=\"T_30917_row5_col3\" class=\"data row5 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row5_col4\" class=\"data row5 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row6\" class=\"row_heading level0 row6\" >Financial Regulators</th>\n",
       "      <td id=\"T_30917_row6_col0\" class=\"data row6 col0\" >44.0</td>\n",
       "      <td id=\"T_30917_row6_col1\" class=\"data row6 col1\" >56.0</td>\n",
       "      <td id=\"T_30917_row6_col2\" class=\"data row6 col2\" >64.8</td>\n",
       "      <td id=\"T_30917_row6_col3\" class=\"data row6 col3\" >94.5</td>\n",
       "      <td id=\"T_30917_row6_col4\" class=\"data row6 col4\" >47.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row7\" class=\"row_heading level0 row7\" >Economic Policy</th>\n",
       "      <td id=\"T_30917_row7_col0\" class=\"data row7 col0\" >92.0</td>\n",
       "      <td id=\"T_30917_row7_col1\" class=\"data row7 col1\" >92.0</td>\n",
       "      <td id=\"T_30917_row7_col2\" class=\"data row7 col2\" >99.5</td>\n",
       "      <td id=\"T_30917_row7_col3\" class=\"data row7 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row7_col4\" class=\"data row7 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row8\" class=\"row_heading level0 row8\" >Windfall Tax</th>\n",
       "      <td id=\"T_30917_row8_col0\" class=\"data row8 col0\" >54.0</td>\n",
       "      <td id=\"T_30917_row8_col1\" class=\"data row8 col1\" >77.0</td>\n",
       "      <td id=\"T_30917_row8_col2\" class=\"data row8 col2\" >99.5</td>\n",
       "      <td id=\"T_30917_row8_col3\" class=\"data row8 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row8_col4\" class=\"data row8 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row9\" class=\"row_heading level0 row9\" >Junk Foods</th>\n",
       "      <td id=\"T_30917_row9_col0\" class=\"data row9 col0\" >83.0</td>\n",
       "      <td id=\"T_30917_row9_col1\" class=\"data row9 col1\" >61.0</td>\n",
       "      <td id=\"T_30917_row9_col2\" class=\"data row9 col2\" >99.0</td>\n",
       "      <td id=\"T_30917_row9_col3\" class=\"data row9 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row9_col4\" class=\"data row9 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row10\" class=\"row_heading level0 row10\" >mean</th>\n",
       "      <td id=\"T_30917_row10_col0\" class=\"data row10 col0\" >58.4</td>\n",
       "      <td id=\"T_30917_row10_col1\" class=\"data row10 col1\" >67.8</td>\n",
       "      <td id=\"T_30917_row10_col2\" class=\"data row10 col2\" >79.7</td>\n",
       "      <td id=\"T_30917_row10_col3\" class=\"data row10 col3\" >89.5</td>\n",
       "      <td id=\"T_30917_row10_col4\" class=\"data row10 col4\" >75.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row11\" class=\"row_heading level0 row11\" >min</th>\n",
       "      <td id=\"T_30917_row11_col0\" class=\"data row11 col0\" >7.0</td>\n",
       "      <td id=\"T_30917_row11_col1\" class=\"data row11 col1\" >35.0</td>\n",
       "      <td id=\"T_30917_row11_col2\" class=\"data row11 col2\" >0.5</td>\n",
       "      <td id=\"T_30917_row11_col3\" class=\"data row11 col3\" >0.0</td>\n",
       "      <td id=\"T_30917_row11_col4\" class=\"data row11 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row12\" class=\"row_heading level0 row12\" >25%</th>\n",
       "      <td id=\"T_30917_row12_col0\" class=\"data row12 col0\" >30.5</td>\n",
       "      <td id=\"T_30917_row12_col1\" class=\"data row12 col1\" >57.3</td>\n",
       "      <td id=\"T_30917_row12_col2\" class=\"data row12 col2\" >73.2</td>\n",
       "      <td id=\"T_30917_row12_col3\" class=\"data row12 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row12_col4\" class=\"data row12 col4\" >60.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row13\" class=\"row_heading level0 row13\" >50%</th>\n",
       "      <td id=\"T_30917_row13_col0\" class=\"data row13 col0\" >62.0</td>\n",
       "      <td id=\"T_30917_row13_col1\" class=\"data row13 col1\" >65.5</td>\n",
       "      <td id=\"T_30917_row13_col2\" class=\"data row13 col2\" >99.2</td>\n",
       "      <td id=\"T_30917_row13_col3\" class=\"data row13 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row13_col4\" class=\"data row13 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row14\" class=\"row_heading level0 row14\" >75%</th>\n",
       "      <td id=\"T_30917_row14_col0\" class=\"data row14 col0\" >89.8</td>\n",
       "      <td id=\"T_30917_row14_col1\" class=\"data row14 col1\" >79.2</td>\n",
       "      <td id=\"T_30917_row14_col2\" class=\"data row14 col2\" >99.5</td>\n",
       "      <td id=\"T_30917_row14_col3\" class=\"data row14 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row14_col4\" class=\"data row14 col4\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_30917_level0_row15\" class=\"row_heading level0 row15\" >max</th>\n",
       "      <td id=\"T_30917_row15_col0\" class=\"data row15 col0\" >100.0</td>\n",
       "      <td id=\"T_30917_row15_col1\" class=\"data row15 col1\" >95.0</td>\n",
       "      <td id=\"T_30917_row15_col2\" class=\"data row15 col2\" >100.0</td>\n",
       "      <td id=\"T_30917_row15_col3\" class=\"data row15 col3\" >100.0</td>\n",
       "      <td id=\"T_30917_row15_col4\" class=\"data row15 col4\" >100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd9c4d6d60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on the 10 Clark Center Survey questions used by Sievertsen and Smith (2023). \n",
      "The data gives, for each question, the share of answers that indicates agreement (agree or totally agree, out of those expressing an opinion).\n",
      "The first column comes from a survey of Economics Profs while the second column comes from a representative survey of 100 people. \n",
      "The third to fifth column come from repeatedly querying different versions of ChatGPT \n"
     ]
    }
   ],
   "source": [
    "display(pd.concat([claimform10.filter(like='Agree', axis=1), claimform10.filter(like='Agree', axis=1).describe().loc[['mean','min','25%','50%', '75%','max']]], axis=0).style.format(precision=1).set_caption(\"Table 5 - % Agree, Out of Those Expressing an Opinion\").set_table_styles(styles))\n",
    "print('Notes: this table is based on the 10 Clark Center Survey questions used by Sievertsen and Smith (2023). \\nThe data gives, for each question, the share of answers that indicates agreement (agree or totally agree, out of those expressing an opinion).\\nThe first column comes from a survey of Economics Profs while the second column comes from a representative survey of 100 people. \\nThe third to fifth column come from repeatedly querying different versions of ChatGPT ')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "152b3475-049e-4a2a-b0a6-fd17dd8b87f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_1d586 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_1d586 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_1d586 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_1d586 .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_1d586\">\n",
       "  <caption>Table A2 - 10 Claims from Sievertsen and Smith (2023)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_1d586_level0_col0\" class=\"col_heading level0 col0\" >Claims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row0\" class=\"row_heading level0 row0\" >AI</th>\n",
       "      <td id=\"T_1d586_row0_col0\" class=\"data row0 col0\" >Use of artificial intelligence over the next ten years will lead to a substantial increase in the growth rates of real per capita income in the US and Western Europe over the subsequent two decades.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row1\" class=\"row_heading level0 row1\" >Twitter</th>\n",
       "      <td id=\"T_1d586_row1_col0\" class=\"data row1 col0\" >There needs to be more government regulation around Twitter’s content moderation and personal data protection.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row2\" class=\"row_heading level0 row2\" >Price Gouging</th>\n",
       "      <td id=\"T_1d586_row2_col0\" class=\"data row2 col0\" >It would serve the US economy well to make it unlawful for companies with revenues over $1 billion to offer goods or services for sale at an excessive price during an exceptional market shock.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row3\" class=\"row_heading level0 row3\" >Net-zero</th>\n",
       "      <td id=\"T_1d586_row3_col0\" class=\"data row3 col0\" >Efforts to achieve the goal of reaching net-zero emissions of greenhouse gases by 2050 will be a major drag on global economic growth.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row4\" class=\"row_heading level0 row4\" >Semi-conductors</th>\n",
       "      <td id=\"T_1d586_row4_col0\" class=\"data row4 col0\" >Given the centrality of semiconductors to the manufacturing of many products, securing reliable supplies should be a key strategic objective of national policy.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row5\" class=\"row_heading level0 row5\" >Greedflation</th>\n",
       "      <td id=\"T_1d586_row5_col0\" class=\"data row5 col0\" >A significant factor behind today’s higher US inflation is dominant corporations in uncompetitive markets taking advantage of their market power to raise prices.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row6\" class=\"row_heading level0 row6\" >Financial Regulators</th>\n",
       "      <td id=\"T_1d586_row6_col0\" class=\"data row6 col0\" >Financial regulators in the US and Europe lack the tools and authority to deter runs on banks by uninsured depositors.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row7\" class=\"row_heading level0 row7\" >Economic Policy</th>\n",
       "      <td id=\"T_1d586_row7_col0\" class=\"data row7 col0\" >When economic policy-makers are unable to commit credibly in advance to a specific decision rule, they will often follow a poor policy trajectory.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row8\" class=\"row_heading level0 row8\" >Windfall Tax</th>\n",
       "      <td id=\"T_1d586_row8_col0\" class=\"data row8 col0\" >A windfall tax on the profits of large oil companies‚ with the revenue rebated to households‚ would provide an efficient means to protect the average US household.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_1d586_level0_row9\" class=\"row_heading level0 row9\" >Junk Foods</th>\n",
       "      <td id=\"T_1d586_row9_col0\" class=\"data row9 col0\" >A ban on advertising junk foods (those that are high in sugar, salt, and fat) would be an effective policy to reduce child obesity.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd5f5b8ee0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "claimform10a=pd.Series(claimsorder10).rename('Claims').to_frame()\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='AI'\n",
    "claimform10a.at[1,'Name']='Twitter'\n",
    "claimform10a.at[2,'Name']='Price Gouging'\n",
    "claimform10a.at[3,'Name']='Net-zero'\n",
    "claimform10a.at[4,'Name']='Semi-conductors'\n",
    "claimform10a.at[5,'Name']='Greedflation'\n",
    "claimform10a.at[6,'Name']='Financial Regulators'\n",
    "claimform10a.at[7,'Name']='Economic Policy'\n",
    "claimform10a.at[8,'Name']='Windfall Tax'\n",
    "claimform10a.at[9,'Name']='Junk Foods'\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "claimform10a.style.set_caption(\"Table A2 - 10 Claims from Sievertsen and Smith (2023)\").set_table_styles(styles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57839de0-f273-4d71-9fa1-a90c035e1ae1",
   "metadata": {},
   "source": [
    "# Part C: Geide-Stevenson and Alvaro La Parra Perez"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "3b77df9b-daa4-4cbd-959c-ca6fac44735d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Proposition</th>\n",
       "      <th>2021\\rN=1422</th>\n",
       "      <th>Disagree</th>\n",
       "      <th>Agree in Proviso</th>\n",
       "      <th>Agree</th>\n",
       "      <th>ε</th>\n",
       "      <th>Agree/Disagree</th>\n",
       "      <th>indicator</th>\n",
       "      <th>Nr</th>\n",
       "      <th>Claim</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.  Flexible and floating exchange rates offer...</td>\n",
       "      <td>2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong</td>\n",
       "      <td>2.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>69.2</td>\n",
       "      <td>.64</td>\n",
       "      <td>98/02</td>\n",
       "      <td>Strong</td>\n",
       "      <td>1</td>\n",
       "      <td>Flexible and floating exchange rates offer an ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.  Tariffs and import quotas usually reduce g...</td>\n",
       "      <td>5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong</td>\n",
       "      <td>5.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>.69</td>\n",
       "      <td>95/05</td>\n",
       "      <td>Strong</td>\n",
       "      <td>2</td>\n",
       "      <td>Tariffs and import quotas usually reduce gener...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.  Some restrictions on the flow of financial...</td>\n",
       "      <td>24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>24.6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>3</td>\n",
       "      <td>Some restrictions on the flow of financial cap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.  The economic benefits of an expanding worl...</td>\n",
       "      <td>42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate</td>\n",
       "      <td>42.4</td>\n",
       "      <td>32.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>.98</td>\n",
       "      <td>58/42</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>4</td>\n",
       "      <td>The economic benefits of an expanding world po...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.  The persistent U.S. trade deficit is due p...</td>\n",
       "      <td>77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong</td>\n",
       "      <td>77.3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>.62</td>\n",
       "      <td>23/77</td>\n",
       "      <td>Strong</td>\n",
       "      <td>5</td>\n",
       "      <td>The persistent U.S. trade deficit is due prima...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.  A large balance of trade deficit has an ad...</td>\n",
       "      <td>65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.</td>\n",
       "      <td>65.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>.77</td>\n",
       "      <td>35/65</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>6</td>\n",
       "      <td>A large balance of trade deficit has an advers...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.  An economy that operates below potential G...</td>\n",
       "      <td>48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate</td>\n",
       "      <td>48.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>.9</td>\n",
       "      <td>52/48</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>7</td>\n",
       "      <td>An economy that operates below potential GDP h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.  There is a natural rate of unemployment to...</td>\n",
       "      <td>26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>.99</td>\n",
       "      <td>74/26</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>8</td>\n",
       "      <td>There is a natural rate of unemployment to whi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9. The Federal Reserve has the capacity to ach...</td>\n",
       "      <td>25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>25.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>34.8</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>9</td>\n",
       "      <td>The Federal Reserve has the capacity to achiev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.  Changes in aggregate demand affect real G...</td>\n",
       "      <td>34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone</td>\n",
       "      <td>34.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>Changes in aggregate demand affect real GDP in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.  The level of government spending relative...</td>\n",
       "      <td>57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate</td>\n",
       "      <td>57.3</td>\n",
       "      <td>19.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>.89</td>\n",
       "      <td>43/57</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "      <td>The level of government spending relative to G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.  Macro models based on the assumption of a...</td>\n",
       "      <td>43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate</td>\n",
       "      <td>43.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>.91</td>\n",
       "      <td>57/43</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>12</td>\n",
       "      <td>Macro models based on the assumption of a “rep...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.  In the short run, a reduction in unemploy...</td>\n",
       "      <td>50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>.89</td>\n",
       "      <td>50/50</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>13</td>\n",
       "      <td>In the short run, a reduction in unemployment ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.  If the federal budget is to be balanced, ...</td>\n",
       "      <td>7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>68.3</td>\n",
       "      <td>.72</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>14</td>\n",
       "      <td>If the federal budget is to be balanced, it sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.  A large federal budget deficit has an adv...</td>\n",
       "      <td>38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate</td>\n",
       "      <td>38.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>.96</td>\n",
       "      <td>61/39</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>15</td>\n",
       "      <td>A large federal budget deficit has an adverse ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.  Fiscal policy (e.g. tax cut and/or expend...</td>\n",
       "      <td>5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong</td>\n",
       "      <td>5.9</td>\n",
       "      <td>31.5</td>\n",
       "      <td>62.6</td>\n",
       "      <td>.75</td>\n",
       "      <td>94/6</td>\n",
       "      <td>Strong</td>\n",
       "      <td>16</td>\n",
       "      <td>Fiscal policy (e.g. tax cut and/or expenditure...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.  Appropriately designed fiscal policy can ...</td>\n",
       "      <td>9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong</td>\n",
       "      <td>9.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>.79</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>17</td>\n",
       "      <td>Appropriately designed fiscal policy can incre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.  Management of the business cycle should b...</td>\n",
       "      <td>66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.</td>\n",
       "      <td>66.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>.78</td>\n",
       "      <td>33/67</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>18</td>\n",
       "      <td>Management of the business cycle should be lef...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.  Inflation is caused primarily by too much...</td>\n",
       "      <td>29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.</td>\n",
       "      <td>29.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>1</td>\n",
       "      <td>71/29</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>19</td>\n",
       "      <td>Inflation is caused primarily by too much grow...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20. The distribution of income in the U.S. sho...</td>\n",
       "      <td>14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>65.2</td>\n",
       "      <td>.80</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>20</td>\n",
       "      <td>The distribution of income in the U.S. should ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21. The Federal Reserve should focus on a low ...</td>\n",
       "      <td>61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate</td>\n",
       "      <td>61.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>.85</td>\n",
       "      <td>38/62</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "      <td>The Federal Reserve should focus on a low rate...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22. The Earned Income Tax Credit program shoul...</td>\n",
       "      <td>9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.</td>\n",
       "      <td>9.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>.82</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>22</td>\n",
       "      <td>The Earned Income Tax Credit program should be...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23. During the pandemic, there is a trade-off ...</td>\n",
       "      <td>43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate</td>\n",
       "      <td>43.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>33.9</td>\n",
       "      <td>.97</td>\n",
       "      <td>56/44</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>23</td>\n",
       "      <td>During the pandemic, there is a trade-off betw...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.  The distribution of income and wealth has...</td>\n",
       "      <td>77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong</td>\n",
       "      <td>77.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>22/78</td>\n",
       "      <td>Strong</td>\n",
       "      <td>24</td>\n",
       "      <td>The distribution of income and wealth has litt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.  Immigration generally has a net positive ...</td>\n",
       "      <td>3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>.56</td>\n",
       "      <td>97/3</td>\n",
       "      <td>Strong</td>\n",
       "      <td>25</td>\n",
       "      <td>Immigration generally has a net positive econo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.  Redistribution of income is a legitimate ...</td>\n",
       "      <td>13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.</td>\n",
       "      <td>13.7</td>\n",
       "      <td>22.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>.81</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>26</td>\n",
       "      <td>Redistribution of income is a legitimate role ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.  Climate change poses a major risk to the ...</td>\n",
       "      <td>14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>71.7</td>\n",
       "      <td>.72</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>27</td>\n",
       "      <td>Climate change poses a major risk to the US ec...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.  A minimum wage increases unemployment amo...</td>\n",
       "      <td>35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>28</td>\n",
       "      <td>A minimum wage increases unemployment among yo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.  Welfare reforms which place time limits o...</td>\n",
       "      <td>45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate</td>\n",
       "      <td>45.9</td>\n",
       "      <td>32.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>.96</td>\n",
       "      <td>54/46</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>29</td>\n",
       "      <td>Welfare reforms which place time limits on pub...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.  The competitive model is generally more u...</td>\n",
       "      <td>53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>.90</td>\n",
       "      <td>47/53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>30</td>\n",
       "      <td>The competitive model is generally more useful...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31.  Pollution taxes or marketable pollution p...</td>\n",
       "      <td>12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.</td>\n",
       "      <td>12.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>.84</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>31</td>\n",
       "      <td>Pollution taxes or marketable pollution permit...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32.  Easing restrictions on immigration will d...</td>\n",
       "      <td>63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.</td>\n",
       "      <td>63.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>32</td>\n",
       "      <td>Easing restrictions on immigration will depres...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33.  The long run benefits of higher taxes on ...</td>\n",
       "      <td>11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong</td>\n",
       "      <td>11.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>73.1</td>\n",
       "      <td>.70</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>33</td>\n",
       "      <td>The long run benefits of higher taxes on fossi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34.  Antitrust laws should be enforced vigorou...</td>\n",
       "      <td>7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.73</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>34</td>\n",
       "      <td>Antitrust laws should be enforced vigorously.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35.  Reducing the tax rate on income from capi...</td>\n",
       "      <td>53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>25.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.92</td>\n",
       "      <td>46/54</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>35</td>\n",
       "      <td>Reducing the tax rate on income from capital g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36.  There are few gender compensation and pro...</td>\n",
       "      <td>58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate</td>\n",
       "      <td>58.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>41/59</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>36</td>\n",
       "      <td>There are few gender compensation and promotio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37. Reducing the regulatory power of the Envir...</td>\n",
       "      <td>74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.68</td>\n",
       "      <td>26/74</td>\n",
       "      <td>Strong</td>\n",
       "      <td>37</td>\n",
       "      <td>Reducing the regulatory power of the Environme...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38. Lower marginal income tax rates increase t...</td>\n",
       "      <td>48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate</td>\n",
       "      <td>48.7</td>\n",
       "      <td>33.8</td>\n",
       "      <td>17.5</td>\n",
       "      <td>.93</td>\n",
       "      <td>51/49</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>38</td>\n",
       "      <td>Lower marginal income tax rates increase the t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39. The structural U.S. federal deficit should...</td>\n",
       "      <td>36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate</td>\n",
       "      <td>36.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>24.2</td>\n",
       "      <td>.98</td>\n",
       "      <td>64/36</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>39</td>\n",
       "      <td>The structural U.S. federal deficit should be ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40. The increasing inequality in the distribut...</td>\n",
       "      <td>64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.</td>\n",
       "      <td>64.1</td>\n",
       "      <td>25.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>40</td>\n",
       "      <td>The increasing inequality in the distribution ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41. Addressing biases in individuals and insti...</td>\n",
       "      <td>10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0.78</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>41</td>\n",
       "      <td>Addressing biases in individuals and instituti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42. Differences in economic outcomes between w...</td>\n",
       "      <td>22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.8</td>\n",
       "      <td>54.1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>42</td>\n",
       "      <td>Differences in economic outcomes between white...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43. Corporate economic power has become too co...</td>\n",
       "      <td>14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.</td>\n",
       "      <td>14.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0.83</td>\n",
       "      <td>85/15</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>43</td>\n",
       "      <td>Corporate economic power has become too concen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44. Lab experiments and randomized controlled ...</td>\n",
       "      <td>22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.</td>\n",
       "      <td>22.4</td>\n",
       "      <td>45.3</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.96</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>44</td>\n",
       "      <td>Lab experiments and randomized controlled tria...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45. Universal health insurance coverage will i...</td>\n",
       "      <td>12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong</td>\n",
       "      <td>12.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.76</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>45</td>\n",
       "      <td>Universal health insurance coverage will incre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46. The US economy provides sufficient opportu...</td>\n",
       "      <td>52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate</td>\n",
       "      <td>52.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.92</td>\n",
       "      <td>48/52</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>46</td>\n",
       "      <td>The US economy provides sufficient opportuniti...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Proposition  \\\n",
       "0   1.  Flexible and floating exchange rates offer...   \n",
       "1   2.  Tariffs and import quotas usually reduce g...   \n",
       "2   3.  Some restrictions on the flow of financial...   \n",
       "3   4.  The economic benefits of an expanding worl...   \n",
       "4   5.  The persistent U.S. trade deficit is due p...   \n",
       "5   6.  A large balance of trade deficit has an ad...   \n",
       "6   7.  An economy that operates below potential G...   \n",
       "7   8.  There is a natural rate of unemployment to...   \n",
       "8   9. The Federal Reserve has the capacity to ach...   \n",
       "9   10.  Changes in aggregate demand affect real G...   \n",
       "10  11.  The level of government spending relative...   \n",
       "11  12.  Macro models based on the assumption of a...   \n",
       "12  13.  In the short run, a reduction in unemploy...   \n",
       "13  14.  If the federal budget is to be balanced, ...   \n",
       "14  15.  A large federal budget deficit has an adv...   \n",
       "15  16.  Fiscal policy (e.g. tax cut and/or expend...   \n",
       "16  17.  Appropriately designed fiscal policy can ...   \n",
       "17  18.  Management of the business cycle should b...   \n",
       "18  19.  Inflation is caused primarily by too much...   \n",
       "19  20. The distribution of income in the U.S. sho...   \n",
       "20  21. The Federal Reserve should focus on a low ...   \n",
       "21  22. The Earned Income Tax Credit program shoul...   \n",
       "22  23. During the pandemic, there is a trade-off ...   \n",
       "23  24.  The distribution of income and wealth has...   \n",
       "24  25.  Immigration generally has a net positive ...   \n",
       "25  26.  Redistribution of income is a legitimate ...   \n",
       "26  27.  Climate change poses a major risk to the ...   \n",
       "27  28.  A minimum wage increases unemployment amo...   \n",
       "28  29.  Welfare reforms which place time limits o...   \n",
       "29  30.  The competitive model is generally more u...   \n",
       "30  31.  Pollution taxes or marketable pollution p...   \n",
       "31  32.  Easing restrictions on immigration will d...   \n",
       "32  33.  The long run benefits of higher taxes on ...   \n",
       "33  34.  Antitrust laws should be enforced vigorou...   \n",
       "34  35.  Reducing the tax rate on income from capi...   \n",
       "35  36.  There are few gender compensation and pro...   \n",
       "36  37. Reducing the regulatory power of the Envir...   \n",
       "37  38. Lower marginal income tax rates increase t...   \n",
       "38  39. The structural U.S. federal deficit should...   \n",
       "39  40. The increasing inequality in the distribut...   \n",
       "40  41. Addressing biases in individuals and insti...   \n",
       "41  42. Differences in economic outcomes between w...   \n",
       "42  43. Corporate economic power has become too co...   \n",
       "43  44. Lab experiments and randomized controlled ...   \n",
       "44  45. Universal health insurance coverage will i...   \n",
       "45  46. The US economy provides sufficient opportu...   \n",
       "\n",
       "                               2021\\rN=1422  Disagree  Agree in Proviso  \\\n",
       "0       2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong       2.4              28.3   \n",
       "1       5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong       5.3              25.4   \n",
       "2      24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.      24.6              39.8   \n",
       "3    42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate      42.4              32.5   \n",
       "4       77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong      77.3              14.5   \n",
       "5       65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.      65.2              25.9   \n",
       "6     48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate      48.1              38.9   \n",
       "7      26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.      26.0              38.8   \n",
       "8      25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.      25.3              39.9   \n",
       "9          34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone      34.9              31.7   \n",
       "10   57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate      57.3              19.7   \n",
       "11   43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate      43.2              42.5   \n",
       "12   50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate      50.0              37.6   \n",
       "13       7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong       7.0              24.7   \n",
       "14   38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate      38.6              41.7   \n",
       "15       5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong       5.9              31.5   \n",
       "16      9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong       9.6              27.0   \n",
       "17     66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.      66.6              21.2   \n",
       "18       29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.      29.2              36.9   \n",
       "19     14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong      14.2              20.6   \n",
       "20   61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate      61.6              20.5   \n",
       "21      9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.       9.9              30.0   \n",
       "22   43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate      43.7              22.4   \n",
       "23     77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong      77.7              16.2   \n",
       "24       3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong       3.0              19.4   \n",
       "25     13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.      13.7              22.3   \n",
       "26     14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong      14.0              14.3   \n",
       "27     35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate      35.0              35.1   \n",
       "28   45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate      45.9              32.7   \n",
       "29   53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate      53.5              30.1   \n",
       "30     12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.      12.2              27.8   \n",
       "31    63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.      63.8              24.3   \n",
       "32     11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong      11.9              15.0   \n",
       "33      7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong       7.0              25.2   \n",
       "34  53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate      53.5              25.9   \n",
       "35  58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate      58.6              20.6   \n",
       "36    74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong      74.0              15.3   \n",
       "37   48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate      48.7              33.8   \n",
       "38   36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate      36.5              39.4   \n",
       "39    64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.      64.1              25.4   \n",
       "40    10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong      10.0              25.3   \n",
       "41    22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.      22.1              23.8   \n",
       "42    14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.      14.8              22.6   \n",
       "43    22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.      22.4              45.3   \n",
       "44    12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong      12.2              19.2   \n",
       "45  52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate      52.3              30.0   \n",
       "\n",
       "    Agree     ε Agree/Disagree indicator  Nr  \\\n",
       "0    69.2   .64          98/02    Strong   1   \n",
       "1    69.3   .69          95/05    Strong   2   \n",
       "2    35.6   .98          75/25    Subst.   3   \n",
       "3    25.0   .98          58/42  Moderate   4   \n",
       "4     8.2   .62          23/77    Strong   5   \n",
       "5     8.9   .77          35/65    Subst.   6   \n",
       "6    12.9    .9          52/48  Moderate   7   \n",
       "7    35.2   .99          74/26    Subst.   8   \n",
       "8    34.8   .98          75/25    Subst.   9   \n",
       "9    33.4     1          65/35      None  10   \n",
       "10   23.0   .89          43/57  Moderate  11   \n",
       "11   14.3   .91          57/43  Moderate  12   \n",
       "12   12.4   .89          50/50  Moderate  13   \n",
       "13   68.3   .72           93/7    Strong  14   \n",
       "14   19.7   .96          61/39  Moderate  15   \n",
       "15   62.6   .75           94/6    Strong  16   \n",
       "16   63.4   .79          90/10    Strong  17   \n",
       "17   12.2   .78          33/67    Subst.  18   \n",
       "18   33.9     1          71/29    Subst.  19   \n",
       "19   65.2   .80          86/14    Strong  20   \n",
       "20   18.0   .85          38/62  Moderate  21   \n",
       "21   60.1   .82          90/10    Subst.  22   \n",
       "22   33.9   .97          56/44  Moderate  23   \n",
       "23    6.1  0.60          22/78    Strong  24   \n",
       "24   77.6   .56           97/3    Strong  25   \n",
       "25   64.0   .81          86/14    Subst.  26   \n",
       "26   71.7   .72          86/14    Strong  27   \n",
       "27   29.8     1          65/35  Moderate  28   \n",
       "28   21.4   .96          54/46  Moderate  29   \n",
       "29   16.4   .90          47/53  Moderate  30   \n",
       "30   60.0   .84          88/12    Subst.  31   \n",
       "31   11.9  0.80          36/64    Subst.  32   \n",
       "32   73.1   .70          88/12    Strong  33   \n",
       "33   67.8  0.73           93/7    Strong  34   \n",
       "34   20.6  0.92          46/54  Moderate  35   \n",
       "35   20.8  0.88          41/59  Moderate  36   \n",
       "36   10.6  0.68          26/74    Strong  37   \n",
       "37   17.5   .93          51/49  Moderate  38   \n",
       "38   24.2   .98          64/36  Moderate  39   \n",
       "39   10.5  0.79          36/64    Subst.  40   \n",
       "40   64.8  0.78          90/10    Strong  41   \n",
       "41   54.1  0.92          78/22    Subst.  42   \n",
       "42   62.6  0.83          85/15    Subst.  43   \n",
       "43   32.2  0.96          78/22    Subst.  44   \n",
       "44   68.6  0.76          88/12    Strong  45   \n",
       "45   17.7  0.92          48/52  Moderate  46   \n",
       "\n",
       "                                                Claim  \n",
       "0   Flexible and floating exchange rates offer an ...  \n",
       "1   Tariffs and import quotas usually reduce gener...  \n",
       "2   Some restrictions on the flow of financial cap...  \n",
       "3   The economic benefits of an expanding world po...  \n",
       "4   The persistent U.S. trade deficit is due prima...  \n",
       "5   A large balance of trade deficit has an advers...  \n",
       "6   An economy that operates below potential GDP h...  \n",
       "7   There is a natural rate of unemployment to whi...  \n",
       "8   The Federal Reserve has the capacity to achiev...  \n",
       "9   Changes in aggregate demand affect real GDP in...  \n",
       "10  The level of government spending relative to G...  \n",
       "11  Macro models based on the assumption of a “rep...  \n",
       "12  In the short run, a reduction in unemployment ...  \n",
       "13  If the federal budget is to be balanced, it sh...  \n",
       "14  A large federal budget deficit has an adverse ...  \n",
       "15  Fiscal policy (e.g. tax cut and/or expenditure...  \n",
       "16  Appropriately designed fiscal policy can incre...  \n",
       "17  Management of the business cycle should be lef...  \n",
       "18  Inflation is caused primarily by too much grow...  \n",
       "19  The distribution of income in the U.S. should ...  \n",
       "20  The Federal Reserve should focus on a low rate...  \n",
       "21  The Earned Income Tax Credit program should be...  \n",
       "22  During the pandemic, there is a trade-off betw...  \n",
       "23  The distribution of income and wealth has litt...  \n",
       "24  Immigration generally has a net positive econo...  \n",
       "25  Redistribution of income is a legitimate role ...  \n",
       "26  Climate change poses a major risk to the US ec...  \n",
       "27  A minimum wage increases unemployment among yo...  \n",
       "28  Welfare reforms which place time limits on pub...  \n",
       "29  The competitive model is generally more useful...  \n",
       "30  Pollution taxes or marketable pollution permit...  \n",
       "31  Easing restrictions on immigration will depres...  \n",
       "32  The long run benefits of higher taxes on fossi...  \n",
       "33      Antitrust laws should be enforced vigorously.  \n",
       "34  Reducing the tax rate on income from capital g...  \n",
       "35  There are few gender compensation and promotio...  \n",
       "36  Reducing the regulatory power of the Environme...  \n",
       "37  Lower marginal income tax rates increase the t...  \n",
       "38  The structural U.S. federal deficit should be ...  \n",
       "39  The increasing inequality in the distribution ...  \n",
       "40  Addressing biases in individuals and instituti...  \n",
       "41  Differences in economic outcomes between white...  \n",
       "42  Corporate economic power has become too concen...  \n",
       "43  Lab experiments and randomized controlled tria...  \n",
       "44  Universal health insurance coverage will incre...  \n",
       "45  The US economy provides sufficient opportuniti...  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# claims of Geide-Stevenson and Alvaro La Parra Perez (forthcoming))\n",
    "alltables=pd.read_pickle('alltables')\n",
    "\n",
    "alltables['Disagree']=alltables['Disagree'].astype(float)\n",
    "alltables['Agree']=alltables['Agree'].astype(float)\n",
    "alltables['Agree in Proviso']=alltables['Agree in Proviso'].astype(float)\n",
    "\n",
    "alltables\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e1db0ed2-6698-433b-92ae-6ca5e8ab94fc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "196\n",
      "197\n",
      "198\n",
      "199\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'claimanswer' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[31], line 58\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m     56\u001b[0m df\u001b[38;5;241m=\u001b[39mpd\u001b[38;5;241m.\u001b[39mDataFrame()\n\u001b[0;32m---> 58\u001b[0m dfecon[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclaimanswer\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m\u001b[43mclaimanswer\u001b[49m\n\u001b[1;32m     59\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclaimquestionanswergpt4o\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mclaimquestionanswergpt4o\n\u001b[1;32m     60\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclaimquestionanswergpt4oprof\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39mclaimquestionanswergpt4oprof\n",
      "\u001b[0;31mNameError\u001b[0m: name 'claimanswer' is not defined"
     ]
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "\n",
    "n=200\n",
    "\n",
    "claimtexts=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "claimquestionanswergpt35=[]\n",
    "\n",
    "for j in range(0,len(alltables)):\n",
    "    print(j)\n",
    "    claimtext=alltables['Claim'][j]\n",
    "    claimtexts+=[claimtext]\n",
    "    for i in range(0,n):\n",
    "        print(i)\n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: agree, agree in proviso, disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "   .     \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "\n",
    "\n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: agree, agree in proviso, disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-3.5-turbo\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        claimtextquestion= claimtext + ' - express your opinion on this statement by choosing among: agree, agree in proviso, disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are a member of the American Economic Association'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "\n",
    "df['claimtext']=np.repeat(claimtexts,n)\n",
    "df.to_pickle('claimsGeide09082024')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "7e1376ce-5cb6-44f6-9cbf-2000d609e8cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claimquestionanswergpt4oclean\n",
      "agree in proviso    3568\n",
      "agree               3376\n",
      "disagree            2256\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt35clean\n",
      "agree               6572\n",
      "disagree            2264\n",
      "agree in proviso     364\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4oprofclean\n",
      "agree in proviso    4370\n",
      "agree               2635\n",
      "disagree            2192\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "#df=pd.read_pickle('claimsAI200')\n",
    "df=pd.read_pickle('claimsGeide09082024')\n",
    "n=200\n",
    "\n",
    "df['claimquestionanswergpt4oclean']=None\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('disagree', case=False)]='disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('proviso', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('provisio', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('provido', case=False)]='agree in proviso'\n",
    "\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('agree', case=False) & (df['claimquestionanswergpt4o'].str.contains('provisioagree', case=False)==False) & (df['claimquestionanswergpt4o'].str.contains('provido', case=False)==False) & (df['claimquestionanswergpt4o'].str.contains('provisio', case=False)==False) & (df['claimquestionanswergpt4o'].str.contains('proviso', case=False)==False) & (df['claimquestionanswergpt4o'].str.contains('disagree', case=False)==False)]='agree'\n",
    "print(df['claimquestionanswergpt4oclean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt35clean']=None\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('disagree', case=False)]='disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('proviso', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('provisio', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('provido', case=False)]='agree in proviso'\n",
    "\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('agree', case=False) & (df['claimquestionanswergpt35'].str.contains('provisioagree', case=False)==False) & (df['claimquestionanswergpt35'].str.contains('provido', case=False)==False) & (df['claimquestionanswergpt35'].str.contains('provisio', case=False)==False) & (df['claimquestionanswergpt35'].str.contains('proviso', case=False)==False) & (df['claimquestionanswergpt35'].str.contains('disagree', case=False)==False)]='agree'\n",
    "print(df['claimquestionanswergpt35clean'].value_counts())\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean']=None\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)]='disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('proviso', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('provisio', case=False)]='agree in proviso'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('provido', case=False)]='agree in proviso'\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('agree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('provisioagree', case=False)==False) & (df['claimquestionanswergpt4oprof'].str.contains('provido', case=False)==False) & (df['claimquestionanswergpt4oprof'].str.contains('provisio', case=False)==False) & (df['claimquestionanswergpt4oprof'].str.contains('proviso', case=False)==False) & (df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)==False)]='agree'\n",
    "print(df['claimquestionanswergpt4oprofclean'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "d775d86e-f58c-427f-8e4d-21c7af25ee41",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:7: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:8: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
      "/tmp/ipykernel_281993/95035809.py:9: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  claimquestionanswergpt4o1  claimquestionanswergpt4oprof1  \\\n",
      "disagree                                NaN                            NaN   \n",
      "agree                                   0.5                            1.5   \n",
      "agree in proviso                       99.5                           98.5   \n",
      "\n",
      "                  claimquestionanswergpt351  claimquestionanswergpt4o2  \\\n",
      "disagree                                1.0                        NaN   \n",
      "agree                                  96.5                       96.5   \n",
      "agree in proviso                        2.5                        3.5   \n",
      "\n",
      "                  claimquestionanswergpt4oprof2  claimquestionanswergpt352  \\\n",
      "disagree                                    NaN                       28.5   \n",
      "agree                                     100.0                       71.0   \n",
      "agree in proviso                            NaN                        0.5   \n",
      "\n",
      "                  claimquestionanswergpt4o3  claimquestionanswergpt4oprof3  \\\n",
      "disagree                                NaN                            NaN   \n",
      "agree                                  51.5                            9.5   \n",
      "agree in proviso                       48.5                           90.5   \n",
      "\n",
      "                  claimquestionanswergpt353  claimquestionanswergpt4o4  ...  \\\n",
      "disagree                                NaN                       82.0  ...   \n",
      "agree                                  96.0                        NaN  ...   \n",
      "agree in proviso                        4.0                       18.0  ...   \n",
      "\n",
      "                  claimquestionanswergpt3543  claimquestionanswergpt4o44  \\\n",
      "disagree                                 2.0                         NaN   \n",
      "agree                                   95.0                        26.5   \n",
      "agree in proviso                         3.0                        73.5   \n",
      "\n",
      "                  claimquestionanswergpt4oprof44  claimquestionanswergpt3544  \\\n",
      "disagree                                     NaN                         NaN   \n",
      "agree                                        4.5                       100.0   \n",
      "agree in proviso                            95.5                         NaN   \n",
      "\n",
      "                  claimquestionanswergpt4o45  claimquestionanswergpt4oprof45  \\\n",
      "disagree                                 NaN                             NaN   \n",
      "agree                                   69.0                            24.5   \n",
      "agree in proviso                        31.0                            75.5   \n",
      "\n",
      "                  claimquestionanswergpt3545  claimquestionanswergpt4o46  \\\n",
      "disagree                                 0.5                         6.0   \n",
      "agree                                   98.5                         NaN   \n",
      "agree in proviso                         1.0                        94.0   \n",
      "\n",
      "                  claimquestionanswergpt4oprof46  claimquestionanswergpt3546  \n",
      "disagree                                    21.5                         7.0  \n",
      "agree                                        NaN                        67.0  \n",
      "agree in proviso                            78.5                        26.0  \n",
      "\n",
      "[3 rows x 138 columns]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Proposition</th>\n",
       "      <th>2021\\rN=1422</th>\n",
       "      <th>Disagree</th>\n",
       "      <th>Agree in Proviso</th>\n",
       "      <th>Agree</th>\n",
       "      <th>ε</th>\n",
       "      <th>Agree/Disagree</th>\n",
       "      <th>indicator</th>\n",
       "      <th>Nr</th>\n",
       "      <th>Claim</th>\n",
       "      <th>ChatGPT4o Agree</th>\n",
       "      <th>ChatGPT4o Agree in Proviso</th>\n",
       "      <th>ChatGPT4o Disagree</th>\n",
       "      <th>ChatGPT4oProf Agree</th>\n",
       "      <th>ChatGPT4oProf Agree in Proviso</th>\n",
       "      <th>ChatGPT4oProf Disagree</th>\n",
       "      <th>ChatGPT35 Agree</th>\n",
       "      <th>ChatGPT35 Agree in Proviso</th>\n",
       "      <th>ChatGPT35 Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.  Flexible and floating exchange rates offer...</td>\n",
       "      <td>2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong</td>\n",
       "      <td>2.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>69.2</td>\n",
       "      <td>.64</td>\n",
       "      <td>98/02</td>\n",
       "      <td>Strong</td>\n",
       "      <td>1</td>\n",
       "      <td>Flexible and floating exchange rates offer an ...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.  Tariffs and import quotas usually reduce g...</td>\n",
       "      <td>5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong</td>\n",
       "      <td>5.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>.69</td>\n",
       "      <td>95/05</td>\n",
       "      <td>Strong</td>\n",
       "      <td>2</td>\n",
       "      <td>Tariffs and import quotas usually reduce gener...</td>\n",
       "      <td>96.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>28.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.  Some restrictions on the flow of financial...</td>\n",
       "      <td>24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>24.6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>3</td>\n",
       "      <td>Some restrictions on the flow of financial cap...</td>\n",
       "      <td>51.5</td>\n",
       "      <td>48.5</td>\n",
       "      <td>0</td>\n",
       "      <td>9.5</td>\n",
       "      <td>90.5</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.  The economic benefits of an expanding worl...</td>\n",
       "      <td>42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate</td>\n",
       "      <td>42.4</td>\n",
       "      <td>32.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>.98</td>\n",
       "      <td>58/42</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>4</td>\n",
       "      <td>The economic benefits of an expanding world po...</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>38.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.  The persistent U.S. trade deficit is due p...</td>\n",
       "      <td>77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong</td>\n",
       "      <td>77.3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>.62</td>\n",
       "      <td>23/77</td>\n",
       "      <td>Strong</td>\n",
       "      <td>5</td>\n",
       "      <td>The persistent U.S. trade deficit is due prima...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.  A large balance of trade deficit has an ad...</td>\n",
       "      <td>65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.</td>\n",
       "      <td>65.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>.77</td>\n",
       "      <td>35/65</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>6</td>\n",
       "      <td>A large balance of trade deficit has an advers...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.  An economy that operates below potential G...</td>\n",
       "      <td>48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate</td>\n",
       "      <td>48.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>.9</td>\n",
       "      <td>52/48</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>7</td>\n",
       "      <td>An economy that operates below potential GDP h...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>63.5</td>\n",
       "      <td>17.5</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.  There is a natural rate of unemployment to...</td>\n",
       "      <td>26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>.99</td>\n",
       "      <td>74/26</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>8</td>\n",
       "      <td>There is a natural rate of unemployment to whi...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.5</td>\n",
       "      <td>50.5</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9. The Federal Reserve has the capacity to ach...</td>\n",
       "      <td>25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>25.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>34.8</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>9</td>\n",
       "      <td>The Federal Reserve has the capacity to achiev...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>93.5</td>\n",
       "      <td>0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>72.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.  Changes in aggregate demand affect real G...</td>\n",
       "      <td>34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone</td>\n",
       "      <td>34.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>Changes in aggregate demand affect real GDP in...</td>\n",
       "      <td>90.5</td>\n",
       "      <td>9.5</td>\n",
       "      <td>0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>19.5</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.  The level of government spending relative...</td>\n",
       "      <td>57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate</td>\n",
       "      <td>57.3</td>\n",
       "      <td>19.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>.89</td>\n",
       "      <td>43/57</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "      <td>The level of government spending relative to G...</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.  Macro models based on the assumption of a...</td>\n",
       "      <td>43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate</td>\n",
       "      <td>43.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>.91</td>\n",
       "      <td>57/43</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>12</td>\n",
       "      <td>Macro models based on the assumption of a “rep...</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>41.0</td>\n",
       "      <td>19.5</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.  In the short run, a reduction in unemploy...</td>\n",
       "      <td>50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>.89</td>\n",
       "      <td>50/50</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>13</td>\n",
       "      <td>In the short run, a reduction in unemployment ...</td>\n",
       "      <td>57.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>14.5</td>\n",
       "      <td>24.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.  If the federal budget is to be balanced, ...</td>\n",
       "      <td>7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>68.3</td>\n",
       "      <td>.72</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>14</td>\n",
       "      <td>If the federal budget is to be balanced, it sh...</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>16.5</td>\n",
       "      <td>0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.  A large federal budget deficit has an adv...</td>\n",
       "      <td>38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate</td>\n",
       "      <td>38.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>.96</td>\n",
       "      <td>61/39</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>15</td>\n",
       "      <td>A large federal budget deficit has an adverse ...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.  Fiscal policy (e.g. tax cut and/or expend...</td>\n",
       "      <td>5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong</td>\n",
       "      <td>5.9</td>\n",
       "      <td>31.5</td>\n",
       "      <td>62.6</td>\n",
       "      <td>.75</td>\n",
       "      <td>94/6</td>\n",
       "      <td>Strong</td>\n",
       "      <td>16</td>\n",
       "      <td>Fiscal policy (e.g. tax cut and/or expenditure...</td>\n",
       "      <td>60.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>42.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.  Appropriately designed fiscal policy can ...</td>\n",
       "      <td>9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong</td>\n",
       "      <td>9.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>.79</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>17</td>\n",
       "      <td>Appropriately designed fiscal policy can incre...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.5</td>\n",
       "      <td>83.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.  Management of the business cycle should b...</td>\n",
       "      <td>66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.</td>\n",
       "      <td>66.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>.78</td>\n",
       "      <td>33/67</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>18</td>\n",
       "      <td>Management of the business cycle should be lef...</td>\n",
       "      <td>0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>82.5</td>\n",
       "      <td>0</td>\n",
       "      <td>27.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.5</td>\n",
       "      <td>46.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.  Inflation is caused primarily by too much...</td>\n",
       "      <td>29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.</td>\n",
       "      <td>29.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>1</td>\n",
       "      <td>71/29</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>19</td>\n",
       "      <td>Inflation is caused primarily by too much grow...</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>73.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20. The distribution of income in the U.S. sho...</td>\n",
       "      <td>14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>65.2</td>\n",
       "      <td>.80</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>20</td>\n",
       "      <td>The distribution of income in the U.S. should ...</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>55.5</td>\n",
       "      <td>44.5</td>\n",
       "      <td>0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21. The Federal Reserve should focus on a low ...</td>\n",
       "      <td>61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate</td>\n",
       "      <td>61.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>.85</td>\n",
       "      <td>38/62</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "      <td>The Federal Reserve should focus on a low rate...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>10.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22. The Earned Income Tax Credit program shoul...</td>\n",
       "      <td>9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.</td>\n",
       "      <td>9.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>.82</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>22</td>\n",
       "      <td>The Earned Income Tax Credit program should be...</td>\n",
       "      <td>98.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23. During the pandemic, there is a trade-off ...</td>\n",
       "      <td>43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate</td>\n",
       "      <td>43.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>33.9</td>\n",
       "      <td>.97</td>\n",
       "      <td>56/44</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>23</td>\n",
       "      <td>During the pandemic, there is a trade-off betw...</td>\n",
       "      <td>21.5</td>\n",
       "      <td>78.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.  The distribution of income and wealth has...</td>\n",
       "      <td>77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong</td>\n",
       "      <td>77.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>22/78</td>\n",
       "      <td>Strong</td>\n",
       "      <td>24</td>\n",
       "      <td>The distribution of income and wealth has litt...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.  Immigration generally has a net positive ...</td>\n",
       "      <td>3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>.56</td>\n",
       "      <td>97/3</td>\n",
       "      <td>Strong</td>\n",
       "      <td>25</td>\n",
       "      <td>Immigration generally has a net positive econo...</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.  Redistribution of income is a legitimate ...</td>\n",
       "      <td>13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.</td>\n",
       "      <td>13.7</td>\n",
       "      <td>22.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>.81</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>26</td>\n",
       "      <td>Redistribution of income is a legitimate role ...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.  Climate change poses a major risk to the ...</td>\n",
       "      <td>14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>71.7</td>\n",
       "      <td>.72</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>27</td>\n",
       "      <td>Climate change poses a major risk to the US ec...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.  A minimum wage increases unemployment amo...</td>\n",
       "      <td>35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>28</td>\n",
       "      <td>A minimum wage increases unemployment among yo...</td>\n",
       "      <td>0</td>\n",
       "      <td>32.5</td>\n",
       "      <td>67.5</td>\n",
       "      <td>0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.  Welfare reforms which place time limits o...</td>\n",
       "      <td>45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate</td>\n",
       "      <td>45.9</td>\n",
       "      <td>32.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>.96</td>\n",
       "      <td>54/46</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>29</td>\n",
       "      <td>Welfare reforms which place time limits on pub...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>12.5</td>\n",
       "      <td>13.5</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.  The competitive model is generally more u...</td>\n",
       "      <td>53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>.90</td>\n",
       "      <td>47/53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>30</td>\n",
       "      <td>The competitive model is generally more useful...</td>\n",
       "      <td>0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31.  Pollution taxes or marketable pollution p...</td>\n",
       "      <td>12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.</td>\n",
       "      <td>12.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>.84</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>31</td>\n",
       "      <td>Pollution taxes or marketable pollution permit...</td>\n",
       "      <td>79.5</td>\n",
       "      <td>20.5</td>\n",
       "      <td>0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>88.5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32.  Easing restrictions on immigration will d...</td>\n",
       "      <td>63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.</td>\n",
       "      <td>63.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>32</td>\n",
       "      <td>Easing restrictions on immigration will depres...</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33.  The long run benefits of higher taxes on ...</td>\n",
       "      <td>11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong</td>\n",
       "      <td>11.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>73.1</td>\n",
       "      <td>.70</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>33</td>\n",
       "      <td>The long run benefits of higher taxes on fossi...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>92.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34.  Antitrust laws should be enforced vigorou...</td>\n",
       "      <td>7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.73</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>34</td>\n",
       "      <td>Antitrust laws should be enforced vigorously.</td>\n",
       "      <td>97.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35.  Reducing the tax rate on income from capi...</td>\n",
       "      <td>53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>25.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.92</td>\n",
       "      <td>46/54</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>35</td>\n",
       "      <td>Reducing the tax rate on income from capital g...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36.  There are few gender compensation and pro...</td>\n",
       "      <td>58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate</td>\n",
       "      <td>58.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>41/59</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>36</td>\n",
       "      <td>There are few gender compensation and promotio...</td>\n",
       "      <td>4.5</td>\n",
       "      <td>52.5</td>\n",
       "      <td>43.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37. Reducing the regulatory power of the Envir...</td>\n",
       "      <td>74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.68</td>\n",
       "      <td>26/74</td>\n",
       "      <td>Strong</td>\n",
       "      <td>37</td>\n",
       "      <td>Reducing the regulatory power of the Environme...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38. Lower marginal income tax rates increase t...</td>\n",
       "      <td>48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate</td>\n",
       "      <td>48.7</td>\n",
       "      <td>33.8</td>\n",
       "      <td>17.5</td>\n",
       "      <td>.93</td>\n",
       "      <td>51/49</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>38</td>\n",
       "      <td>Lower marginal income tax rates increase the t...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>86.5</td>\n",
       "      <td>11.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39. The structural U.S. federal deficit should...</td>\n",
       "      <td>36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate</td>\n",
       "      <td>36.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>24.2</td>\n",
       "      <td>.98</td>\n",
       "      <td>64/36</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>39</td>\n",
       "      <td>The structural U.S. federal deficit should be ...</td>\n",
       "      <td>19.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40. The increasing inequality in the distribut...</td>\n",
       "      <td>64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.</td>\n",
       "      <td>64.1</td>\n",
       "      <td>25.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>40</td>\n",
       "      <td>The increasing inequality in the distribution ...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41. Addressing biases in individuals and insti...</td>\n",
       "      <td>10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0.78</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>41</td>\n",
       "      <td>Addressing biases in individuals and instituti...</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42. Differences in economic outcomes between w...</td>\n",
       "      <td>22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.8</td>\n",
       "      <td>54.1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>42</td>\n",
       "      <td>Differences in economic outcomes between white...</td>\n",
       "      <td>99.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43. Corporate economic power has become too co...</td>\n",
       "      <td>14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.</td>\n",
       "      <td>14.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0.83</td>\n",
       "      <td>85/15</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>43</td>\n",
       "      <td>Corporate economic power has become too concen...</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44. Lab experiments and randomized controlled ...</td>\n",
       "      <td>22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.</td>\n",
       "      <td>22.4</td>\n",
       "      <td>45.3</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.96</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>44</td>\n",
       "      <td>Lab experiments and randomized controlled tria...</td>\n",
       "      <td>26.5</td>\n",
       "      <td>73.5</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45. Universal health insurance coverage will i...</td>\n",
       "      <td>12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong</td>\n",
       "      <td>12.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.76</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>45</td>\n",
       "      <td>Universal health insurance coverage will incre...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.5</td>\n",
       "      <td>75.5</td>\n",
       "      <td>0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46. The US economy provides sufficient opportu...</td>\n",
       "      <td>52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate</td>\n",
       "      <td>52.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.92</td>\n",
       "      <td>48/52</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>46</td>\n",
       "      <td>The US economy provides sufficient opportuniti...</td>\n",
       "      <td>0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>21.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Proposition  \\\n",
       "0   1.  Flexible and floating exchange rates offer...   \n",
       "1   2.  Tariffs and import quotas usually reduce g...   \n",
       "2   3.  Some restrictions on the flow of financial...   \n",
       "3   4.  The economic benefits of an expanding worl...   \n",
       "4   5.  The persistent U.S. trade deficit is due p...   \n",
       "5   6.  A large balance of trade deficit has an ad...   \n",
       "6   7.  An economy that operates below potential G...   \n",
       "7   8.  There is a natural rate of unemployment to...   \n",
       "8   9. The Federal Reserve has the capacity to ach...   \n",
       "9   10.  Changes in aggregate demand affect real G...   \n",
       "10  11.  The level of government spending relative...   \n",
       "11  12.  Macro models based on the assumption of a...   \n",
       "12  13.  In the short run, a reduction in unemploy...   \n",
       "13  14.  If the federal budget is to be balanced, ...   \n",
       "14  15.  A large federal budget deficit has an adv...   \n",
       "15  16.  Fiscal policy (e.g. tax cut and/or expend...   \n",
       "16  17.  Appropriately designed fiscal policy can ...   \n",
       "17  18.  Management of the business cycle should b...   \n",
       "18  19.  Inflation is caused primarily by too much...   \n",
       "19  20. The distribution of income in the U.S. sho...   \n",
       "20  21. The Federal Reserve should focus on a low ...   \n",
       "21  22. The Earned Income Tax Credit program shoul...   \n",
       "22  23. During the pandemic, there is a trade-off ...   \n",
       "23  24.  The distribution of income and wealth has...   \n",
       "24  25.  Immigration generally has a net positive ...   \n",
       "25  26.  Redistribution of income is a legitimate ...   \n",
       "26  27.  Climate change poses a major risk to the ...   \n",
       "27  28.  A minimum wage increases unemployment amo...   \n",
       "28  29.  Welfare reforms which place time limits o...   \n",
       "29  30.  The competitive model is generally more u...   \n",
       "30  31.  Pollution taxes or marketable pollution p...   \n",
       "31  32.  Easing restrictions on immigration will d...   \n",
       "32  33.  The long run benefits of higher taxes on ...   \n",
       "33  34.  Antitrust laws should be enforced vigorou...   \n",
       "34  35.  Reducing the tax rate on income from capi...   \n",
       "35  36.  There are few gender compensation and pro...   \n",
       "36  37. Reducing the regulatory power of the Envir...   \n",
       "37  38. Lower marginal income tax rates increase t...   \n",
       "38  39. The structural U.S. federal deficit should...   \n",
       "39  40. The increasing inequality in the distribut...   \n",
       "40  41. Addressing biases in individuals and insti...   \n",
       "41  42. Differences in economic outcomes between w...   \n",
       "42  43. Corporate economic power has become too co...   \n",
       "43  44. Lab experiments and randomized controlled ...   \n",
       "44  45. Universal health insurance coverage will i...   \n",
       "45  46. The US economy provides sufficient opportu...   \n",
       "\n",
       "                               2021\\rN=1422  Disagree  Agree in Proviso  \\\n",
       "0       2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong       2.4              28.3   \n",
       "1       5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong       5.3              25.4   \n",
       "2      24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.      24.6              39.8   \n",
       "3    42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate      42.4              32.5   \n",
       "4       77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong      77.3              14.5   \n",
       "5       65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.      65.2              25.9   \n",
       "6     48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate      48.1              38.9   \n",
       "7      26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.      26.0              38.8   \n",
       "8      25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.      25.3              39.9   \n",
       "9          34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone      34.9              31.7   \n",
       "10   57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate      57.3              19.7   \n",
       "11   43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate      43.2              42.5   \n",
       "12   50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate      50.0              37.6   \n",
       "13       7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong       7.0              24.7   \n",
       "14   38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate      38.6              41.7   \n",
       "15       5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong       5.9              31.5   \n",
       "16      9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong       9.6              27.0   \n",
       "17     66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.      66.6              21.2   \n",
       "18       29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.      29.2              36.9   \n",
       "19     14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong      14.2              20.6   \n",
       "20   61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate      61.6              20.5   \n",
       "21      9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.       9.9              30.0   \n",
       "22   43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate      43.7              22.4   \n",
       "23     77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong      77.7              16.2   \n",
       "24       3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong       3.0              19.4   \n",
       "25     13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.      13.7              22.3   \n",
       "26     14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong      14.0              14.3   \n",
       "27     35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate      35.0              35.1   \n",
       "28   45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate      45.9              32.7   \n",
       "29   53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate      53.5              30.1   \n",
       "30     12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.      12.2              27.8   \n",
       "31    63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.      63.8              24.3   \n",
       "32     11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong      11.9              15.0   \n",
       "33      7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong       7.0              25.2   \n",
       "34  53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate      53.5              25.9   \n",
       "35  58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate      58.6              20.6   \n",
       "36    74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong      74.0              15.3   \n",
       "37   48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate      48.7              33.8   \n",
       "38   36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate      36.5              39.4   \n",
       "39    64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.      64.1              25.4   \n",
       "40    10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong      10.0              25.3   \n",
       "41    22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.      22.1              23.8   \n",
       "42    14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.      14.8              22.6   \n",
       "43    22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.      22.4              45.3   \n",
       "44    12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong      12.2              19.2   \n",
       "45  52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate      52.3              30.0   \n",
       "\n",
       "    Agree     ε Agree/Disagree indicator  Nr  \\\n",
       "0    69.2   .64          98/02    Strong   1   \n",
       "1    69.3   .69          95/05    Strong   2   \n",
       "2    35.6   .98          75/25    Subst.   3   \n",
       "3    25.0   .98          58/42  Moderate   4   \n",
       "4     8.2   .62          23/77    Strong   5   \n",
       "5     8.9   .77          35/65    Subst.   6   \n",
       "6    12.9    .9          52/48  Moderate   7   \n",
       "7    35.2   .99          74/26    Subst.   8   \n",
       "8    34.8   .98          75/25    Subst.   9   \n",
       "9    33.4     1          65/35      None  10   \n",
       "10   23.0   .89          43/57  Moderate  11   \n",
       "11   14.3   .91          57/43  Moderate  12   \n",
       "12   12.4   .89          50/50  Moderate  13   \n",
       "13   68.3   .72           93/7    Strong  14   \n",
       "14   19.7   .96          61/39  Moderate  15   \n",
       "15   62.6   .75           94/6    Strong  16   \n",
       "16   63.4   .79          90/10    Strong  17   \n",
       "17   12.2   .78          33/67    Subst.  18   \n",
       "18   33.9     1          71/29    Subst.  19   \n",
       "19   65.2   .80          86/14    Strong  20   \n",
       "20   18.0   .85          38/62  Moderate  21   \n",
       "21   60.1   .82          90/10    Subst.  22   \n",
       "22   33.9   .97          56/44  Moderate  23   \n",
       "23    6.1  0.60          22/78    Strong  24   \n",
       "24   77.6   .56           97/3    Strong  25   \n",
       "25   64.0   .81          86/14    Subst.  26   \n",
       "26   71.7   .72          86/14    Strong  27   \n",
       "27   29.8     1          65/35  Moderate  28   \n",
       "28   21.4   .96          54/46  Moderate  29   \n",
       "29   16.4   .90          47/53  Moderate  30   \n",
       "30   60.0   .84          88/12    Subst.  31   \n",
       "31   11.9  0.80          36/64    Subst.  32   \n",
       "32   73.1   .70          88/12    Strong  33   \n",
       "33   67.8  0.73           93/7    Strong  34   \n",
       "34   20.6  0.92          46/54  Moderate  35   \n",
       "35   20.8  0.88          41/59  Moderate  36   \n",
       "36   10.6  0.68          26/74    Strong  37   \n",
       "37   17.5   .93          51/49  Moderate  38   \n",
       "38   24.2   .98          64/36  Moderate  39   \n",
       "39   10.5  0.79          36/64    Subst.  40   \n",
       "40   64.8  0.78          90/10    Strong  41   \n",
       "41   54.1  0.92          78/22    Subst.  42   \n",
       "42   62.6  0.83          85/15    Subst.  43   \n",
       "43   32.2  0.96          78/22    Subst.  44   \n",
       "44   68.6  0.76          88/12    Strong  45   \n",
       "45   17.7  0.92          48/52  Moderate  46   \n",
       "\n",
       "                                                Claim ChatGPT4o Agree  \\\n",
       "0   Flexible and floating exchange rates offer an ...             0.5   \n",
       "1   Tariffs and import quotas usually reduce gener...            96.5   \n",
       "2   Some restrictions on the flow of financial cap...            51.5   \n",
       "3   The economic benefits of an expanding world po...               0   \n",
       "4   The persistent U.S. trade deficit is due prima...               0   \n",
       "5   A large balance of trade deficit has an advers...            24.0   \n",
       "6   An economy that operates below potential GDP h...             4.5   \n",
       "7   There is a natural rate of unemployment to whi...            88.0   \n",
       "8   The Federal Reserve has the capacity to achiev...             6.5   \n",
       "9   Changes in aggregate demand affect real GDP in...            90.5   \n",
       "10  The level of government spending relative to G...               0   \n",
       "11  Macro models based on the assumption of a “rep...               0   \n",
       "12  In the short run, a reduction in unemployment ...            57.5   \n",
       "13  If the federal budget is to be balanced, it sh...            82.0   \n",
       "14  A large federal budget deficit has an adverse ...             2.5   \n",
       "15  Fiscal policy (e.g. tax cut and/or expenditure...            60.5   \n",
       "16  Appropriately designed fiscal policy can incre...            10.0   \n",
       "17  Management of the business cycle should be lef...               0   \n",
       "18  Inflation is caused primarily by too much grow...               0   \n",
       "19  The distribution of income in the U.S. should ...            97.5   \n",
       "20  The Federal Reserve should focus on a low rate...               0   \n",
       "21  The Earned Income Tax Credit program should be...            98.5   \n",
       "22  During the pandemic, there is a trade-off betw...            21.5   \n",
       "23  The distribution of income and wealth has litt...               0   \n",
       "24  Immigration generally has a net positive econo...            99.5   \n",
       "25  Redistribution of income is a legitimate role ...             4.5   \n",
       "26  Climate change poses a major risk to the US ec...           100.0   \n",
       "27  A minimum wage increases unemployment among yo...               0   \n",
       "28  Welfare reforms which place time limits on pub...               0   \n",
       "29  The competitive model is generally more useful...               0   \n",
       "30  Pollution taxes or marketable pollution permit...            79.5   \n",
       "31  Easing restrictions on immigration will depres...               0   \n",
       "32  The long run benefits of higher taxes on fossi...            96.0   \n",
       "33      Antitrust laws should be enforced vigorously.            97.0   \n",
       "34  Reducing the tax rate on income from capital g...             1.0   \n",
       "35  There are few gender compensation and promotio...             4.5   \n",
       "36  Reducing the regulatory power of the Environme...               0   \n",
       "37  Lower marginal income tax rates increase the t...             2.0   \n",
       "38  The structural U.S. federal deficit should be ...            19.0   \n",
       "39  The increasing inequality in the distribution ...             2.5   \n",
       "40  Addressing biases in individuals and instituti...            97.5   \n",
       "41  Differences in economic outcomes between white...            99.5   \n",
       "42  Corporate economic power has become too concen...            98.0   \n",
       "43  Lab experiments and randomized controlled tria...            26.5   \n",
       "44  Universal health insurance coverage will incre...            69.0   \n",
       "45  The US economy provides sufficient opportuniti...               0   \n",
       "\n",
       "   ChatGPT4o Agree in Proviso ChatGPT4o Disagree ChatGPT4oProf Agree  \\\n",
       "0                        99.5                  0                 1.5   \n",
       "1                         3.5                  0               100.0   \n",
       "2                        48.5                  0                 9.5   \n",
       "3                        18.0               82.0                   0   \n",
       "4                           0              100.0                   0   \n",
       "5                        76.0                  0                   0   \n",
       "6                        95.5                  0                   0   \n",
       "7                        12.0                  0                49.5   \n",
       "8                        93.5                  0                 7.5   \n",
       "9                         9.5                  0                80.5   \n",
       "10                       44.0               56.0                   0   \n",
       "11                       13.0               87.0                   0   \n",
       "12                       42.5                  0                 4.0   \n",
       "13                       18.0                  0                83.5   \n",
       "14                       97.5                  0                   0   \n",
       "15                       39.5                  0                57.5   \n",
       "16                       90.0                  0                16.5   \n",
       "17                       17.5               82.5                   0   \n",
       "18                       96.0                4.0                   0   \n",
       "19                        2.5                  0                55.5   \n",
       "20                          0              100.0                   0   \n",
       "21                        1.5                  0                86.0   \n",
       "22                       78.5                  0                 4.0   \n",
       "23                          0              100.0                   0   \n",
       "24                        0.5                  0                97.0   \n",
       "25                       95.5                  0                 7.0   \n",
       "26                          0                  0               100.0   \n",
       "27                       32.5               67.5                   0   \n",
       "28                        5.0               95.0                   0   \n",
       "29                        5.5               94.5                   0   \n",
       "30                       20.5                  0                87.0   \n",
       "31                        1.0               99.0                   0   \n",
       "32                        4.0                  0                82.0   \n",
       "33                        3.0                  0                71.0   \n",
       "34                       99.0                  0                   0   \n",
       "35                       52.5               43.0                   0   \n",
       "36                          0              100.0                   0   \n",
       "37                       86.5               11.5                 0.5   \n",
       "38                       81.0                  0                21.0   \n",
       "39                       97.5                  0                   0   \n",
       "40                        2.5                  0                97.5   \n",
       "41                        0.5                  0                94.0   \n",
       "42                        2.0                  0                76.0   \n",
       "43                       73.5                  0                 4.5   \n",
       "44                       31.0                  0                24.5   \n",
       "45                       94.0                6.0                   0   \n",
       "\n",
       "   ChatGPT4oProf Agree in Proviso ChatGPT4oProf Disagree ChatGPT35 Agree  \\\n",
       "0                            98.5                      0            96.5   \n",
       "1                               0                      0            71.0   \n",
       "2                            90.5                      0            96.0   \n",
       "3                            52.5                   47.0            54.0   \n",
       "4                               0                  100.0            21.0   \n",
       "5                            98.5                    0.5            99.5   \n",
       "6                            99.5                    0.5            63.5   \n",
       "7                            50.5                      0            95.0   \n",
       "8                            92.0                    0.5            72.0   \n",
       "9                            19.5                      0             2.5   \n",
       "10                           26.0                   74.0            37.0   \n",
       "11                            3.5                   96.5            41.0   \n",
       "12                           96.0                      0            61.0   \n",
       "13                           16.5                      0            91.5   \n",
       "14                          100.0                      0            99.5   \n",
       "15                           42.5                      0           100.0   \n",
       "16                           83.5                      0           100.0   \n",
       "17                           27.5                   72.5            47.0   \n",
       "18                           89.5                   10.5            73.0   \n",
       "19                           44.5                      0            99.5   \n",
       "20                            0.5                   99.5            10.5   \n",
       "21                           14.0                      0            99.5   \n",
       "22                           96.0                      0            95.0   \n",
       "23                              0                  100.0               0   \n",
       "24                            3.0                      0           100.0   \n",
       "25                           93.0                      0            97.5   \n",
       "26                              0                      0           100.0   \n",
       "27                           99.0                    1.0            17.5   \n",
       "28                           19.0                   81.0            12.5   \n",
       "29                           10.0                   90.0            18.5   \n",
       "30                           13.0                      0            88.5   \n",
       "31                            4.5                   95.5             7.5   \n",
       "32                           18.0                      0            92.5   \n",
       "33                           29.0                      0            99.5   \n",
       "34                          100.0                      0            89.5   \n",
       "35                            3.5                   96.5            91.0   \n",
       "36                              0                  100.0             2.5   \n",
       "37                           95.0                    4.5            95.5   \n",
       "38                           79.0                      0            96.0   \n",
       "39                           95.5                    4.5            94.0   \n",
       "40                            2.5                      0           100.0   \n",
       "41                            6.0                      0            97.5   \n",
       "42                           24.0                      0            95.0   \n",
       "43                           95.5                      0           100.0   \n",
       "44                           75.5                      0            98.5   \n",
       "45                           78.5                   21.5            67.0   \n",
       "\n",
       "   ChatGPT35 Agree in Proviso ChatGPT35 Disagree  \n",
       "0                         2.5                1.0  \n",
       "1                         0.5               28.5  \n",
       "2                         4.0                  0  \n",
       "3                         7.5               38.5  \n",
       "4                           0               79.0  \n",
       "5                           0                0.5  \n",
       "6                        17.5               19.0  \n",
       "7                         4.0                1.0  \n",
       "8                         8.0               20.0  \n",
       "9                         0.5               97.0  \n",
       "10                        3.5               59.5  \n",
       "11                       19.5               39.5  \n",
       "12                       14.5               24.5  \n",
       "13                        4.0                4.5  \n",
       "14                          0                0.5  \n",
       "15                          0                  0  \n",
       "16                          0                  0  \n",
       "17                        6.5               46.5  \n",
       "18                        1.5               25.5  \n",
       "19                          0                0.5  \n",
       "20                        5.0               84.5  \n",
       "21                          0                0.5  \n",
       "22                        3.5                1.5  \n",
       "23                          0              100.0  \n",
       "24                          0                  0  \n",
       "25                        2.0                0.5  \n",
       "26                          0                  0  \n",
       "27                        1.0               81.5  \n",
       "28                       13.5               74.0  \n",
       "29                        1.0               80.5  \n",
       "30                        7.0                4.5  \n",
       "31                        1.0               91.5  \n",
       "32                        5.0                2.5  \n",
       "33                        0.5                  0  \n",
       "34                        9.0                1.5  \n",
       "35                        1.5                7.5  \n",
       "36                          0               97.5  \n",
       "37                        0.5                4.0  \n",
       "38                        2.5                1.5  \n",
       "39                        3.0                3.0  \n",
       "40                          0                  0  \n",
       "41                        2.0                0.5  \n",
       "42                        3.0                2.0  \n",
       "43                          0                  0  \n",
       "44                        1.0                0.5  \n",
       "45                       26.0                7.0  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# overview by question\n",
    "fixedform=pd.DataFrame(index=['disagree','agree', 'agree in proviso'])\n",
    "ct=0\n",
    "for i in alltables['Claim']:\n",
    "    ct=ct+1\n",
    "    dfs=df[df['claimtext']==i]    \n",
    "    fixedform['claimquestionanswergpt4o'+str(ct)]=dfs['claimquestionanswergpt4oclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt4oprof'+str(ct)]=dfs['claimquestionanswergpt4oprofclean'].value_counts()/(n/100)\n",
    "    fixedform['claimquestionanswergpt35'+str(ct)]=dfs['claimquestionanswergpt35clean'].value_counts()/(n/100)\n",
    "print(fixedform)\n",
    "\n",
    "alltables['ChatGPT4o Agree']=None\n",
    "alltables['ChatGPT4o Agree in Proviso']=None\n",
    "alltables['ChatGPT4o Disagree']=None\n",
    "\n",
    "alltables['ChatGPT4oProf Agree']=None\n",
    "alltables['ChatGPT4oProf Agree in Proviso']=None\n",
    "alltables['ChatGPT4oProf Disagree']=None\n",
    "\n",
    "alltables['ChatGPT35 Agree']=None\n",
    "alltables['ChatGPT35 Agree in Proviso']=None\n",
    "alltables['ChatGPT35 Disagree']=None\n",
    "\n",
    "ct=0\n",
    "for i in alltables['Claim']:\n",
    "    dfs=df[df['claimtext']==i]    \n",
    "\n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT4o Disagree']=dfs['claimquestionanswergpt4oclean'].value_counts()['disagree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4o Disagree']=0\n",
    "    try:    \n",
    "        alltables.at[ct,'ChatGPT4o Agree']=dfs['claimquestionanswergpt4oclean'].value_counts()['agree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4o Agree']=0\n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT4o Agree in Proviso']=dfs['claimquestionanswergpt4oclean'].value_counts()['agree in proviso']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4o Agree in Proviso']=0\n",
    "\n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT4oProf Disagree']=dfs['claimquestionanswergpt4oprofclean'].value_counts()['disagree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4oProf Disagree']=0\n",
    "    try:    \n",
    "        alltables.at[ct,'ChatGPT4oProf Agree']=dfs['claimquestionanswergpt4oprofclean'].value_counts()['agree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4oProf Agree']=0\n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT4oProf Agree in Proviso']=dfs['claimquestionanswergpt4oprofclean'].value_counts()['agree in proviso']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT4oProf Agree in Proviso']=0\n",
    "    \n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT35 Disagree']=dfs['claimquestionanswergpt35clean'].value_counts()['disagree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT35 Disagree']=0\n",
    "    try:    \n",
    "        alltables.at[ct,'ChatGPT35 Agree']=dfs['claimquestionanswergpt35clean'].value_counts()['agree']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT35 Agree']=0\n",
    "    try:\n",
    "        alltables.at[ct,'ChatGPT35 Agree in Proviso']=dfs['claimquestionanswergpt35clean'].value_counts()['agree in proviso']/(n/100)\n",
    "    except:\n",
    "        alltables.at[ct,'ChatGPT35 Agree in Proviso']=0\n",
    "    \n",
    "    ct=ct+1\n",
    "alltables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "234d88a8-b88a-4678-b2b2-98fbb135ee29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Proposition</th>\n",
       "      <th>2021\\rN=1422</th>\n",
       "      <th>Disagree</th>\n",
       "      <th>Agree in Proviso</th>\n",
       "      <th>Agree</th>\n",
       "      <th>ε</th>\n",
       "      <th>Agree/Disagree</th>\n",
       "      <th>indicator</th>\n",
       "      <th>Nr</th>\n",
       "      <th>Claim</th>\n",
       "      <th>...</th>\n",
       "      <th>ChatGPT35 Agree in Proviso</th>\n",
       "      <th>ChatGPT35 Disagree</th>\n",
       "      <th>Median</th>\n",
       "      <th>ChatGPT4o Median</th>\n",
       "      <th>ChatGPT4oProf Median</th>\n",
       "      <th>ChatGPT35 Median</th>\n",
       "      <th>MaxValue</th>\n",
       "      <th>ChatGPT4o MaxValue</th>\n",
       "      <th>ChatGPT4oProf MaxValue</th>\n",
       "      <th>ChatGPT35 MaxValue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.  Flexible and floating exchange rates offer...</td>\n",
       "      <td>2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong</td>\n",
       "      <td>2.4</td>\n",
       "      <td>28.3</td>\n",
       "      <td>69.2</td>\n",
       "      <td>.64</td>\n",
       "      <td>98/02</td>\n",
       "      <td>Strong</td>\n",
       "      <td>1</td>\n",
       "      <td>Flexible and floating exchange rates offer an ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>69.2</td>\n",
       "      <td>99.5</td>\n",
       "      <td>98.5</td>\n",
       "      <td>96.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.  Tariffs and import quotas usually reduce g...</td>\n",
       "      <td>5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong</td>\n",
       "      <td>5.3</td>\n",
       "      <td>25.4</td>\n",
       "      <td>69.3</td>\n",
       "      <td>.69</td>\n",
       "      <td>95/05</td>\n",
       "      <td>Strong</td>\n",
       "      <td>2</td>\n",
       "      <td>Tariffs and import quotas usually reduce gener...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>28.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>69.3</td>\n",
       "      <td>96.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.  Some restrictions on the flow of financial...</td>\n",
       "      <td>24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>24.6</td>\n",
       "      <td>39.8</td>\n",
       "      <td>35.6</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>3</td>\n",
       "      <td>Some restrictions on the flow of financial cap...</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>39.8</td>\n",
       "      <td>51.5</td>\n",
       "      <td>90.5</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.  The economic benefits of an expanding worl...</td>\n",
       "      <td>42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate</td>\n",
       "      <td>42.4</td>\n",
       "      <td>32.5</td>\n",
       "      <td>25.0</td>\n",
       "      <td>.98</td>\n",
       "      <td>58/42</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>4</td>\n",
       "      <td>The economic benefits of an expanding world po...</td>\n",
       "      <td>...</td>\n",
       "      <td>7.5</td>\n",
       "      <td>38.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>42.4</td>\n",
       "      <td>82.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.  The persistent U.S. trade deficit is due p...</td>\n",
       "      <td>77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong</td>\n",
       "      <td>77.3</td>\n",
       "      <td>14.5</td>\n",
       "      <td>8.2</td>\n",
       "      <td>.62</td>\n",
       "      <td>23/77</td>\n",
       "      <td>Strong</td>\n",
       "      <td>5</td>\n",
       "      <td>The persistent U.S. trade deficit is due prima...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>77.3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.  A large balance of trade deficit has an ad...</td>\n",
       "      <td>65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.</td>\n",
       "      <td>65.2</td>\n",
       "      <td>25.9</td>\n",
       "      <td>8.9</td>\n",
       "      <td>.77</td>\n",
       "      <td>35/65</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>6</td>\n",
       "      <td>A large balance of trade deficit has an advers...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>65.2</td>\n",
       "      <td>76.0</td>\n",
       "      <td>98.5</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.  An economy that operates below potential G...</td>\n",
       "      <td>48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate</td>\n",
       "      <td>48.1</td>\n",
       "      <td>38.9</td>\n",
       "      <td>12.9</td>\n",
       "      <td>.9</td>\n",
       "      <td>52/48</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>7</td>\n",
       "      <td>An economy that operates below potential GDP h...</td>\n",
       "      <td>...</td>\n",
       "      <td>17.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>48.1</td>\n",
       "      <td>95.5</td>\n",
       "      <td>99.5</td>\n",
       "      <td>63.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.  There is a natural rate of unemployment to...</td>\n",
       "      <td>26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.8</td>\n",
       "      <td>35.2</td>\n",
       "      <td>.99</td>\n",
       "      <td>74/26</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>8</td>\n",
       "      <td>There is a natural rate of unemployment to whi...</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>38.8</td>\n",
       "      <td>88.0</td>\n",
       "      <td>50.5</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9. The Federal Reserve has the capacity to ach...</td>\n",
       "      <td>25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.</td>\n",
       "      <td>25.3</td>\n",
       "      <td>39.9</td>\n",
       "      <td>34.8</td>\n",
       "      <td>.98</td>\n",
       "      <td>75/25</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>9</td>\n",
       "      <td>The Federal Reserve has the capacity to achiev...</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>39.9</td>\n",
       "      <td>93.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>72.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.  Changes in aggregate demand affect real G...</td>\n",
       "      <td>34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone</td>\n",
       "      <td>34.9</td>\n",
       "      <td>31.7</td>\n",
       "      <td>33.4</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>Changes in aggregate demand affect real GDP in...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>34.9</td>\n",
       "      <td>90.5</td>\n",
       "      <td>80.5</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11.  The level of government spending relative...</td>\n",
       "      <td>57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate</td>\n",
       "      <td>57.3</td>\n",
       "      <td>19.7</td>\n",
       "      <td>23.0</td>\n",
       "      <td>.89</td>\n",
       "      <td>43/57</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>11</td>\n",
       "      <td>The level of government spending relative to G...</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>59.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>57.3</td>\n",
       "      <td>56.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>59.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12.  Macro models based on the assumption of a...</td>\n",
       "      <td>43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate</td>\n",
       "      <td>43.2</td>\n",
       "      <td>42.5</td>\n",
       "      <td>14.3</td>\n",
       "      <td>.91</td>\n",
       "      <td>57/43</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>12</td>\n",
       "      <td>Macro models based on the assumption of a “rep...</td>\n",
       "      <td>...</td>\n",
       "      <td>19.5</td>\n",
       "      <td>39.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>43.2</td>\n",
       "      <td>87.0</td>\n",
       "      <td>96.5</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.  In the short run, a reduction in unemploy...</td>\n",
       "      <td>50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate</td>\n",
       "      <td>50.0</td>\n",
       "      <td>37.6</td>\n",
       "      <td>12.4</td>\n",
       "      <td>.89</td>\n",
       "      <td>50/50</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>13</td>\n",
       "      <td>In the short run, a reduction in unemployment ...</td>\n",
       "      <td>...</td>\n",
       "      <td>14.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>50.0</td>\n",
       "      <td>57.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.  If the federal budget is to be balanced, ...</td>\n",
       "      <td>7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>24.7</td>\n",
       "      <td>68.3</td>\n",
       "      <td>.72</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>14</td>\n",
       "      <td>If the federal budget is to be balanced, it sh...</td>\n",
       "      <td>...</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>68.3</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15.  A large federal budget deficit has an adv...</td>\n",
       "      <td>38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate</td>\n",
       "      <td>38.6</td>\n",
       "      <td>41.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>.96</td>\n",
       "      <td>61/39</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>15</td>\n",
       "      <td>A large federal budget deficit has an adverse ...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>41.7</td>\n",
       "      <td>97.5</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16.  Fiscal policy (e.g. tax cut and/or expend...</td>\n",
       "      <td>5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong</td>\n",
       "      <td>5.9</td>\n",
       "      <td>31.5</td>\n",
       "      <td>62.6</td>\n",
       "      <td>.75</td>\n",
       "      <td>94/6</td>\n",
       "      <td>Strong</td>\n",
       "      <td>16</td>\n",
       "      <td>Fiscal policy (e.g. tax cut and/or expenditure...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>62.6</td>\n",
       "      <td>60.5</td>\n",
       "      <td>57.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17.  Appropriately designed fiscal policy can ...</td>\n",
       "      <td>9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong</td>\n",
       "      <td>9.6</td>\n",
       "      <td>27.0</td>\n",
       "      <td>63.4</td>\n",
       "      <td>.79</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>17</td>\n",
       "      <td>Appropriately designed fiscal policy can incre...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>63.4</td>\n",
       "      <td>90.0</td>\n",
       "      <td>83.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.  Management of the business cycle should b...</td>\n",
       "      <td>66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.</td>\n",
       "      <td>66.6</td>\n",
       "      <td>21.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>.78</td>\n",
       "      <td>33/67</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>18</td>\n",
       "      <td>Management of the business cycle should be lef...</td>\n",
       "      <td>...</td>\n",
       "      <td>6.5</td>\n",
       "      <td>46.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>66.6</td>\n",
       "      <td>82.5</td>\n",
       "      <td>72.5</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19.  Inflation is caused primarily by too much...</td>\n",
       "      <td>29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.</td>\n",
       "      <td>29.2</td>\n",
       "      <td>36.9</td>\n",
       "      <td>33.9</td>\n",
       "      <td>1</td>\n",
       "      <td>71/29</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>19</td>\n",
       "      <td>Inflation is caused primarily by too much grow...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>25.5</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>36.9</td>\n",
       "      <td>96.0</td>\n",
       "      <td>89.5</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20. The distribution of income in the U.S. sho...</td>\n",
       "      <td>14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong</td>\n",
       "      <td>14.2</td>\n",
       "      <td>20.6</td>\n",
       "      <td>65.2</td>\n",
       "      <td>.80</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>20</td>\n",
       "      <td>The distribution of income in the U.S. should ...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>65.2</td>\n",
       "      <td>97.5</td>\n",
       "      <td>55.5</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21. The Federal Reserve should focus on a low ...</td>\n",
       "      <td>61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate</td>\n",
       "      <td>61.6</td>\n",
       "      <td>20.5</td>\n",
       "      <td>18.0</td>\n",
       "      <td>.85</td>\n",
       "      <td>38/62</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>21</td>\n",
       "      <td>The Federal Reserve should focus on a low rate...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>84.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>61.6</td>\n",
       "      <td>100.0</td>\n",
       "      <td>99.5</td>\n",
       "      <td>84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22. The Earned Income Tax Credit program shoul...</td>\n",
       "      <td>9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.</td>\n",
       "      <td>9.9</td>\n",
       "      <td>30.0</td>\n",
       "      <td>60.1</td>\n",
       "      <td>.82</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>22</td>\n",
       "      <td>The Earned Income Tax Credit program should be...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>60.1</td>\n",
       "      <td>98.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23. During the pandemic, there is a trade-off ...</td>\n",
       "      <td>43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate</td>\n",
       "      <td>43.7</td>\n",
       "      <td>22.4</td>\n",
       "      <td>33.9</td>\n",
       "      <td>.97</td>\n",
       "      <td>56/44</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>23</td>\n",
       "      <td>During the pandemic, there is a trade-off betw...</td>\n",
       "      <td>...</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>43.7</td>\n",
       "      <td>78.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24.  The distribution of income and wealth has...</td>\n",
       "      <td>77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong</td>\n",
       "      <td>77.7</td>\n",
       "      <td>16.2</td>\n",
       "      <td>6.1</td>\n",
       "      <td>0.60</td>\n",
       "      <td>22/78</td>\n",
       "      <td>Strong</td>\n",
       "      <td>24</td>\n",
       "      <td>The distribution of income and wealth has litt...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>77.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25.  Immigration generally has a net positive ...</td>\n",
       "      <td>3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong</td>\n",
       "      <td>3.0</td>\n",
       "      <td>19.4</td>\n",
       "      <td>77.6</td>\n",
       "      <td>.56</td>\n",
       "      <td>97/3</td>\n",
       "      <td>Strong</td>\n",
       "      <td>25</td>\n",
       "      <td>Immigration generally has a net positive econo...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>77.6</td>\n",
       "      <td>99.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.  Redistribution of income is a legitimate ...</td>\n",
       "      <td>13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.</td>\n",
       "      <td>13.7</td>\n",
       "      <td>22.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>.81</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>26</td>\n",
       "      <td>Redistribution of income is a legitimate role ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>64.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>93.0</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27.  Climate change poses a major risk to the ...</td>\n",
       "      <td>14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong</td>\n",
       "      <td>14.0</td>\n",
       "      <td>14.3</td>\n",
       "      <td>71.7</td>\n",
       "      <td>.72</td>\n",
       "      <td>86/14</td>\n",
       "      <td>Strong</td>\n",
       "      <td>27</td>\n",
       "      <td>Climate change poses a major risk to the US ec...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>71.7</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28.  A minimum wage increases unemployment amo...</td>\n",
       "      <td>35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.1</td>\n",
       "      <td>29.8</td>\n",
       "      <td>1</td>\n",
       "      <td>65/35</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>28</td>\n",
       "      <td>A minimum wage increases unemployment among yo...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81.5</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>35.1</td>\n",
       "      <td>67.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>81.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29.  Welfare reforms which place time limits o...</td>\n",
       "      <td>45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate</td>\n",
       "      <td>45.9</td>\n",
       "      <td>32.7</td>\n",
       "      <td>21.4</td>\n",
       "      <td>.96</td>\n",
       "      <td>54/46</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>29</td>\n",
       "      <td>Welfare reforms which place time limits on pub...</td>\n",
       "      <td>...</td>\n",
       "      <td>13.5</td>\n",
       "      <td>74.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>45.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30.  The competitive model is generally more u...</td>\n",
       "      <td>53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>30.1</td>\n",
       "      <td>16.4</td>\n",
       "      <td>.90</td>\n",
       "      <td>47/53</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>30</td>\n",
       "      <td>The competitive model is generally more useful...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>80.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>53.5</td>\n",
       "      <td>94.5</td>\n",
       "      <td>90.0</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31.  Pollution taxes or marketable pollution p...</td>\n",
       "      <td>12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.</td>\n",
       "      <td>12.2</td>\n",
       "      <td>27.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>.84</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>31</td>\n",
       "      <td>Pollution taxes or marketable pollution permit...</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>60.0</td>\n",
       "      <td>79.5</td>\n",
       "      <td>87.0</td>\n",
       "      <td>88.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32.  Easing restrictions on immigration will d...</td>\n",
       "      <td>63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.</td>\n",
       "      <td>63.8</td>\n",
       "      <td>24.3</td>\n",
       "      <td>11.9</td>\n",
       "      <td>0.80</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>32</td>\n",
       "      <td>Easing restrictions on immigration will depres...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>91.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>63.8</td>\n",
       "      <td>99.0</td>\n",
       "      <td>95.5</td>\n",
       "      <td>91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33.  The long run benefits of higher taxes on ...</td>\n",
       "      <td>11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong</td>\n",
       "      <td>11.9</td>\n",
       "      <td>15.0</td>\n",
       "      <td>73.1</td>\n",
       "      <td>.70</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>33</td>\n",
       "      <td>The long run benefits of higher taxes on fossi...</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>73.1</td>\n",
       "      <td>96.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>92.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34.  Antitrust laws should be enforced vigorou...</td>\n",
       "      <td>7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong</td>\n",
       "      <td>7.0</td>\n",
       "      <td>25.2</td>\n",
       "      <td>67.8</td>\n",
       "      <td>0.73</td>\n",
       "      <td>93/7</td>\n",
       "      <td>Strong</td>\n",
       "      <td>34</td>\n",
       "      <td>Antitrust laws should be enforced vigorously.</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>67.8</td>\n",
       "      <td>97.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35.  Reducing the tax rate on income from capi...</td>\n",
       "      <td>53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate</td>\n",
       "      <td>53.5</td>\n",
       "      <td>25.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0.92</td>\n",
       "      <td>46/54</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>35</td>\n",
       "      <td>Reducing the tax rate on income from capital g...</td>\n",
       "      <td>...</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>53.5</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>89.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36.  There are few gender compensation and pro...</td>\n",
       "      <td>58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate</td>\n",
       "      <td>58.6</td>\n",
       "      <td>20.6</td>\n",
       "      <td>20.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>41/59</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>36</td>\n",
       "      <td>There are few gender compensation and promotio...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>58.6</td>\n",
       "      <td>52.5</td>\n",
       "      <td>96.5</td>\n",
       "      <td>91.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37. Reducing the regulatory power of the Envir...</td>\n",
       "      <td>74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong</td>\n",
       "      <td>74.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>10.6</td>\n",
       "      <td>0.68</td>\n",
       "      <td>26/74</td>\n",
       "      <td>Strong</td>\n",
       "      <td>37</td>\n",
       "      <td>Reducing the regulatory power of the Environme...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>97.5</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>74.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38. Lower marginal income tax rates increase t...</td>\n",
       "      <td>48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate</td>\n",
       "      <td>48.7</td>\n",
       "      <td>33.8</td>\n",
       "      <td>17.5</td>\n",
       "      <td>.93</td>\n",
       "      <td>51/49</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>38</td>\n",
       "      <td>Lower marginal income tax rates increase the t...</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>48.7</td>\n",
       "      <td>86.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39. The structural U.S. federal deficit should...</td>\n",
       "      <td>36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate</td>\n",
       "      <td>36.5</td>\n",
       "      <td>39.4</td>\n",
       "      <td>24.2</td>\n",
       "      <td>.98</td>\n",
       "      <td>64/36</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>39</td>\n",
       "      <td>The structural U.S. federal deficit should be ...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>39.4</td>\n",
       "      <td>81.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40. The increasing inequality in the distribut...</td>\n",
       "      <td>64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.</td>\n",
       "      <td>64.1</td>\n",
       "      <td>25.4</td>\n",
       "      <td>10.5</td>\n",
       "      <td>0.79</td>\n",
       "      <td>36/64</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>40</td>\n",
       "      <td>The increasing inequality in the distribution ...</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>64.1</td>\n",
       "      <td>97.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41. Addressing biases in individuals and insti...</td>\n",
       "      <td>10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.3</td>\n",
       "      <td>64.8</td>\n",
       "      <td>0.78</td>\n",
       "      <td>90/10</td>\n",
       "      <td>Strong</td>\n",
       "      <td>41</td>\n",
       "      <td>Addressing biases in individuals and instituti...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>64.8</td>\n",
       "      <td>97.5</td>\n",
       "      <td>97.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42. Differences in economic outcomes between w...</td>\n",
       "      <td>22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.</td>\n",
       "      <td>22.1</td>\n",
       "      <td>23.8</td>\n",
       "      <td>54.1</td>\n",
       "      <td>0.92</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>42</td>\n",
       "      <td>Differences in economic outcomes between white...</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>54.1</td>\n",
       "      <td>99.5</td>\n",
       "      <td>94.0</td>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43. Corporate economic power has become too co...</td>\n",
       "      <td>14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.</td>\n",
       "      <td>14.8</td>\n",
       "      <td>22.6</td>\n",
       "      <td>62.6</td>\n",
       "      <td>0.83</td>\n",
       "      <td>85/15</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>43</td>\n",
       "      <td>Corporate economic power has become too concen...</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>62.6</td>\n",
       "      <td>98.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44. Lab experiments and randomized controlled ...</td>\n",
       "      <td>22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.</td>\n",
       "      <td>22.4</td>\n",
       "      <td>45.3</td>\n",
       "      <td>32.2</td>\n",
       "      <td>0.96</td>\n",
       "      <td>78/22</td>\n",
       "      <td>Subst.</td>\n",
       "      <td>44</td>\n",
       "      <td>Lab experiments and randomized controlled tria...</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>45.3</td>\n",
       "      <td>73.5</td>\n",
       "      <td>95.5</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45. Universal health insurance coverage will i...</td>\n",
       "      <td>12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong</td>\n",
       "      <td>12.2</td>\n",
       "      <td>19.2</td>\n",
       "      <td>68.6</td>\n",
       "      <td>0.76</td>\n",
       "      <td>88/12</td>\n",
       "      <td>Strong</td>\n",
       "      <td>45</td>\n",
       "      <td>Universal health insurance coverage will incre...</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>68.6</td>\n",
       "      <td>69.0</td>\n",
       "      <td>75.5</td>\n",
       "      <td>98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46. The US economy provides sufficient opportu...</td>\n",
       "      <td>52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate</td>\n",
       "      <td>52.3</td>\n",
       "      <td>30.0</td>\n",
       "      <td>17.7</td>\n",
       "      <td>0.92</td>\n",
       "      <td>48/52</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>46</td>\n",
       "      <td>The US economy provides sufficient opportuniti...</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Disagree</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree in Proviso</td>\n",
       "      <td>Agree</td>\n",
       "      <td>52.3</td>\n",
       "      <td>94.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          Proposition  \\\n",
       "0   1.  Flexible and floating exchange rates offer...   \n",
       "1   2.  Tariffs and import quotas usually reduce g...   \n",
       "2   3.  Some restrictions on the flow of financial...   \n",
       "3   4.  The economic benefits of an expanding worl...   \n",
       "4   5.  The persistent U.S. trade deficit is due p...   \n",
       "5   6.  A large balance of trade deficit has an ad...   \n",
       "6   7.  An economy that operates below potential G...   \n",
       "7   8.  There is a natural rate of unemployment to...   \n",
       "8   9. The Federal Reserve has the capacity to ach...   \n",
       "9   10.  Changes in aggregate demand affect real G...   \n",
       "10  11.  The level of government spending relative...   \n",
       "11  12.  Macro models based on the assumption of a...   \n",
       "12  13.  In the short run, a reduction in unemploy...   \n",
       "13  14.  If the federal budget is to be balanced, ...   \n",
       "14  15.  A large federal budget deficit has an adv...   \n",
       "15  16.  Fiscal policy (e.g. tax cut and/or expend...   \n",
       "16  17.  Appropriately designed fiscal policy can ...   \n",
       "17  18.  Management of the business cycle should b...   \n",
       "18  19.  Inflation is caused primarily by too much...   \n",
       "19  20. The distribution of income in the U.S. sho...   \n",
       "20  21. The Federal Reserve should focus on a low ...   \n",
       "21  22. The Earned Income Tax Credit program shoul...   \n",
       "22  23. During the pandemic, there is a trade-off ...   \n",
       "23  24.  The distribution of income and wealth has...   \n",
       "24  25.  Immigration generally has a net positive ...   \n",
       "25  26.  Redistribution of income is a legitimate ...   \n",
       "26  27.  Climate change poses a major risk to the ...   \n",
       "27  28.  A minimum wage increases unemployment amo...   \n",
       "28  29.  Welfare reforms which place time limits o...   \n",
       "29  30.  The competitive model is generally more u...   \n",
       "30  31.  Pollution taxes or marketable pollution p...   \n",
       "31  32.  Easing restrictions on immigration will d...   \n",
       "32  33.  The long run benefits of higher taxes on ...   \n",
       "33  34.  Antitrust laws should be enforced vigorou...   \n",
       "34  35.  Reducing the tax rate on income from capi...   \n",
       "35  36.  There are few gender compensation and pro...   \n",
       "36  37. Reducing the regulatory power of the Envir...   \n",
       "37  38. Lower marginal income tax rates increase t...   \n",
       "38  39. The structural U.S. federal deficit should...   \n",
       "39  40. The increasing inequality in the distribut...   \n",
       "40  41. Addressing biases in individuals and insti...   \n",
       "41  42. Differences in economic outcomes between w...   \n",
       "42  43. Corporate economic power has become too co...   \n",
       "43  44. Lab experiments and randomized controlled ...   \n",
       "44  45. Universal health insurance coverage will i...   \n",
       "45  46. The US economy provides sufficient opportu...   \n",
       "\n",
       "                               2021\\rN=1422  Disagree  Agree in Proviso  \\\n",
       "0       2.4\\r28.3\\r69.2\\r.64\\r98/02\\rStrong       2.4              28.3   \n",
       "1       5.3\\r25.4\\r69.3\\r.69\\r95/05\\rStrong       5.3              25.4   \n",
       "2      24.6\\r39.8\\r35.6\\r.98\\r75/25\\rSubst.      24.6              39.8   \n",
       "3    42.4\\r32.5\\r25.0\\r.98\\r58/42\\rModerate      42.4              32.5   \n",
       "4       77.3\\r14.5\\r8.2\\r.62\\r23/77\\rStrong      77.3              14.5   \n",
       "5       65.2\\r25.9\\r8.9\\r.77\\r35/65\\rSubst.      65.2              25.9   \n",
       "6     48.1\\r38.9\\r12.9\\r.9\\r52/48\\rModerate      48.1              38.9   \n",
       "7      26.0\\r38.8\\r35.2\\r.99\\r74/26\\rSubst.      26.0              38.8   \n",
       "8      25.3\\r39.9\\r34.8\\r.98\\r75/25\\rSubst.      25.3              39.9   \n",
       "9          34.9\\r31.7\\r33.4\\r1\\r65/35\\rNone      34.9              31.7   \n",
       "10   57.3\\r19.7\\r23.0\\r.89\\r43/57\\rModerate      57.3              19.7   \n",
       "11   43.2\\r42.5\\r14.3\\r.91\\r57/43\\rModerate      43.2              42.5   \n",
       "12   50.0\\r37.6\\r12.4\\r.89\\r50/50\\rModerate      50.0              37.6   \n",
       "13       7.0\\r24.7\\r68.3\\r.72\\r93/7\\rStrong       7.0              24.7   \n",
       "14   38.6\\r41.7\\r19.7\\r.96\\r61/39\\rModerate      38.6              41.7   \n",
       "15       5.9\\r31.5\\r62.6\\r.75\\r94/6\\rStrong       5.9              31.5   \n",
       "16      9.6\\r27.0\\r63.4\\r.79\\r90/10\\rStrong       9.6              27.0   \n",
       "17     66.6\\r21.2\\r12.2\\r.78\\r33/67\\rSubst.      66.6              21.2   \n",
       "18       29.2\\r36.9\\r33.9\\r1\\r71/29\\rSubst.      29.2              36.9   \n",
       "19     14.2\\r20.6\\r65.2\\r.80\\r86/14\\rStrong      14.2              20.6   \n",
       "20   61.6\\r20.5\\r18.0\\r.85\\r38/62\\rModerate      61.6              20.5   \n",
       "21      9.9\\r30.0\\r60.1\\r.82\\r90/10\\rSubst.       9.9              30.0   \n",
       "22   43.7\\r22.4\\r33.9\\r.97\\r56/44\\rModerate      43.7              22.4   \n",
       "23     77.7\\r16.2\\r6.1\\r0.60\\r22/78\\rStrong      77.7              16.2   \n",
       "24       3.0\\r19.4\\r77.6\\r.56\\r97/3\\rStrong       3.0              19.4   \n",
       "25     13.7\\r22.3\\r64.0\\r.81\\r86/14\\rSubst.      13.7              22.3   \n",
       "26     14.0\\r14.3\\r71.7\\r.72\\r86/14\\rStrong      14.0              14.3   \n",
       "27     35.0\\r35.1\\r29.8\\r1\\r65/35\\rModerate      35.0              35.1   \n",
       "28   45.9\\r32.7\\r21.4\\r.96\\r54/46\\rModerate      45.9              32.7   \n",
       "29   53.5\\r30.1\\r16.4\\r.90\\r47/53\\rModerate      53.5              30.1   \n",
       "30     12.2\\r27.8\\r60.0\\r.84\\r88/12\\rSubst.      12.2              27.8   \n",
       "31    63.8\\r24.3\\r11.9\\r0.80\\r36/64\\rSubst.      63.8              24.3   \n",
       "32     11.9\\r15.0\\r73.1\\r.70\\r88/12\\rStrong      11.9              15.0   \n",
       "33      7.0\\r25.2\\r67.8\\r0.73\\r93/7\\rStrong       7.0              25.2   \n",
       "34  53.5\\r25.9\\r20.6\\r0.92\\r46/54\\rModerate      53.5              25.9   \n",
       "35  58.6\\r20.6\\r20.8\\r0.88\\r41/59\\rModerate      58.6              20.6   \n",
       "36    74.0\\r15.3\\r10.6\\r0.68\\r26/74\\rStrong      74.0              15.3   \n",
       "37   48.7\\r33.8\\r17.5\\r.93\\r51/49\\rModerate      48.7              33.8   \n",
       "38   36.5\\r39.4\\r24.2\\r.98\\r64/36\\rModerate      36.5              39.4   \n",
       "39    64.1\\r25.4\\r10.5\\r0.79\\r36/64\\rSubst.      64.1              25.4   \n",
       "40    10.0\\r25.3\\r64.8\\r0.78\\r90/10\\rStrong      10.0              25.3   \n",
       "41    22.1\\r23.8\\r54.1\\r0.92\\r78/22\\rSubst.      22.1              23.8   \n",
       "42    14.8\\r22.6\\r62.6\\r0.83\\r85/15\\rSubst.      14.8              22.6   \n",
       "43    22.4\\r45.3\\r32.2\\r0.96\\r78/22\\rSubst.      22.4              45.3   \n",
       "44    12.2\\r19.2\\r68.6\\r0.76\\r88/12\\rStrong      12.2              19.2   \n",
       "45  52.3\\r30.0\\r17.7\\r0.92\\r48/52\\rModerate      52.3              30.0   \n",
       "\n",
       "    Agree     ε Agree/Disagree indicator  Nr  \\\n",
       "0    69.2   .64          98/02    Strong   1   \n",
       "1    69.3   .69          95/05    Strong   2   \n",
       "2    35.6   .98          75/25    Subst.   3   \n",
       "3    25.0   .98          58/42  Moderate   4   \n",
       "4     8.2   .62          23/77    Strong   5   \n",
       "5     8.9   .77          35/65    Subst.   6   \n",
       "6    12.9    .9          52/48  Moderate   7   \n",
       "7    35.2   .99          74/26    Subst.   8   \n",
       "8    34.8   .98          75/25    Subst.   9   \n",
       "9    33.4     1          65/35      None  10   \n",
       "10   23.0   .89          43/57  Moderate  11   \n",
       "11   14.3   .91          57/43  Moderate  12   \n",
       "12   12.4   .89          50/50  Moderate  13   \n",
       "13   68.3   .72           93/7    Strong  14   \n",
       "14   19.7   .96          61/39  Moderate  15   \n",
       "15   62.6   .75           94/6    Strong  16   \n",
       "16   63.4   .79          90/10    Strong  17   \n",
       "17   12.2   .78          33/67    Subst.  18   \n",
       "18   33.9     1          71/29    Subst.  19   \n",
       "19   65.2   .80          86/14    Strong  20   \n",
       "20   18.0   .85          38/62  Moderate  21   \n",
       "21   60.1   .82          90/10    Subst.  22   \n",
       "22   33.9   .97          56/44  Moderate  23   \n",
       "23    6.1  0.60          22/78    Strong  24   \n",
       "24   77.6   .56           97/3    Strong  25   \n",
       "25   64.0   .81          86/14    Subst.  26   \n",
       "26   71.7   .72          86/14    Strong  27   \n",
       "27   29.8     1          65/35  Moderate  28   \n",
       "28   21.4   .96          54/46  Moderate  29   \n",
       "29   16.4   .90          47/53  Moderate  30   \n",
       "30   60.0   .84          88/12    Subst.  31   \n",
       "31   11.9  0.80          36/64    Subst.  32   \n",
       "32   73.1   .70          88/12    Strong  33   \n",
       "33   67.8  0.73           93/7    Strong  34   \n",
       "34   20.6  0.92          46/54  Moderate  35   \n",
       "35   20.8  0.88          41/59  Moderate  36   \n",
       "36   10.6  0.68          26/74    Strong  37   \n",
       "37   17.5   .93          51/49  Moderate  38   \n",
       "38   24.2   .98          64/36  Moderate  39   \n",
       "39   10.5  0.79          36/64    Subst.  40   \n",
       "40   64.8  0.78          90/10    Strong  41   \n",
       "41   54.1  0.92          78/22    Subst.  42   \n",
       "42   62.6  0.83          85/15    Subst.  43   \n",
       "43   32.2  0.96          78/22    Subst.  44   \n",
       "44   68.6  0.76          88/12    Strong  45   \n",
       "45   17.7  0.92          48/52  Moderate  46   \n",
       "\n",
       "                                                Claim  ...  \\\n",
       "0   Flexible and floating exchange rates offer an ...  ...   \n",
       "1   Tariffs and import quotas usually reduce gener...  ...   \n",
       "2   Some restrictions on the flow of financial cap...  ...   \n",
       "3   The economic benefits of an expanding world po...  ...   \n",
       "4   The persistent U.S. trade deficit is due prima...  ...   \n",
       "5   A large balance of trade deficit has an advers...  ...   \n",
       "6   An economy that operates below potential GDP h...  ...   \n",
       "7   There is a natural rate of unemployment to whi...  ...   \n",
       "8   The Federal Reserve has the capacity to achiev...  ...   \n",
       "9   Changes in aggregate demand affect real GDP in...  ...   \n",
       "10  The level of government spending relative to G...  ...   \n",
       "11  Macro models based on the assumption of a “rep...  ...   \n",
       "12  In the short run, a reduction in unemployment ...  ...   \n",
       "13  If the federal budget is to be balanced, it sh...  ...   \n",
       "14  A large federal budget deficit has an adverse ...  ...   \n",
       "15  Fiscal policy (e.g. tax cut and/or expenditure...  ...   \n",
       "16  Appropriately designed fiscal policy can incre...  ...   \n",
       "17  Management of the business cycle should be lef...  ...   \n",
       "18  Inflation is caused primarily by too much grow...  ...   \n",
       "19  The distribution of income in the U.S. should ...  ...   \n",
       "20  The Federal Reserve should focus on a low rate...  ...   \n",
       "21  The Earned Income Tax Credit program should be...  ...   \n",
       "22  During the pandemic, there is a trade-off betw...  ...   \n",
       "23  The distribution of income and wealth has litt...  ...   \n",
       "24  Immigration generally has a net positive econo...  ...   \n",
       "25  Redistribution of income is a legitimate role ...  ...   \n",
       "26  Climate change poses a major risk to the US ec...  ...   \n",
       "27  A minimum wage increases unemployment among yo...  ...   \n",
       "28  Welfare reforms which place time limits on pub...  ...   \n",
       "29  The competitive model is generally more useful...  ...   \n",
       "30  Pollution taxes or marketable pollution permit...  ...   \n",
       "31  Easing restrictions on immigration will depres...  ...   \n",
       "32  The long run benefits of higher taxes on fossi...  ...   \n",
       "33      Antitrust laws should be enforced vigorously.  ...   \n",
       "34  Reducing the tax rate on income from capital g...  ...   \n",
       "35  There are few gender compensation and promotio...  ...   \n",
       "36  Reducing the regulatory power of the Environme...  ...   \n",
       "37  Lower marginal income tax rates increase the t...  ...   \n",
       "38  The structural U.S. federal deficit should be ...  ...   \n",
       "39  The increasing inequality in the distribution ...  ...   \n",
       "40  Addressing biases in individuals and instituti...  ...   \n",
       "41  Differences in economic outcomes between white...  ...   \n",
       "42  Corporate economic power has become too concen...  ...   \n",
       "43  Lab experiments and randomized controlled tria...  ...   \n",
       "44  Universal health insurance coverage will incre...  ...   \n",
       "45  The US economy provides sufficient opportuniti...  ...   \n",
       "\n",
       "   ChatGPT35 Agree in Proviso ChatGPT35 Disagree            Median  \\\n",
       "0                         2.5                1.0             Agree   \n",
       "1                         0.5               28.5             Agree   \n",
       "2                         4.0                  0  Agree in Proviso   \n",
       "3                         7.5               38.5          Disagree   \n",
       "4                           0               79.0          Disagree   \n",
       "5                           0                0.5          Disagree   \n",
       "6                        17.5               19.0          Disagree   \n",
       "7                         4.0                1.0  Agree in Proviso   \n",
       "8                         8.0               20.0  Agree in Proviso   \n",
       "9                         0.5               97.0          Disagree   \n",
       "10                        3.5               59.5          Disagree   \n",
       "11                       19.5               39.5          Disagree   \n",
       "12                       14.5               24.5          Disagree   \n",
       "13                        4.0                4.5             Agree   \n",
       "14                          0                0.5  Agree in Proviso   \n",
       "15                          0                  0             Agree   \n",
       "16                          0                  0             Agree   \n",
       "17                        6.5               46.5          Disagree   \n",
       "18                        1.5               25.5  Agree in Proviso   \n",
       "19                          0                0.5             Agree   \n",
       "20                        5.0               84.5          Disagree   \n",
       "21                          0                0.5             Agree   \n",
       "22                        3.5                1.5          Disagree   \n",
       "23                          0              100.0          Disagree   \n",
       "24                          0                  0             Agree   \n",
       "25                        2.0                0.5             Agree   \n",
       "26                          0                  0             Agree   \n",
       "27                        1.0               81.5  Agree in Proviso   \n",
       "28                       13.5               74.0          Disagree   \n",
       "29                        1.0               80.5          Disagree   \n",
       "30                        7.0                4.5             Agree   \n",
       "31                        1.0               91.5          Disagree   \n",
       "32                        5.0                2.5             Agree   \n",
       "33                        0.5                  0             Agree   \n",
       "34                        9.0                1.5          Disagree   \n",
       "35                        1.5                7.5          Disagree   \n",
       "36                          0               97.5          Disagree   \n",
       "37                        0.5                4.0          Disagree   \n",
       "38                        2.5                1.5  Agree in Proviso   \n",
       "39                        3.0                3.0          Disagree   \n",
       "40                          0                  0             Agree   \n",
       "41                        2.0                0.5             Agree   \n",
       "42                        3.0                2.0             Agree   \n",
       "43                          0                  0  Agree in Proviso   \n",
       "44                        1.0                0.5             Agree   \n",
       "45                       26.0                7.0          Disagree   \n",
       "\n",
       "    ChatGPT4o Median ChatGPT4oProf Median ChatGPT35 Median MaxValue  \\\n",
       "0   Agree in Proviso     Agree in Proviso            Agree     69.2   \n",
       "1              Agree                Agree            Agree     69.3   \n",
       "2              Agree     Agree in Proviso            Agree     39.8   \n",
       "3           Disagree     Agree in Proviso            Agree     42.4   \n",
       "4           Disagree             Disagree         Disagree     77.3   \n",
       "5   Agree in Proviso     Agree in Proviso            Agree     65.2   \n",
       "6   Agree in Proviso     Agree in Proviso            Agree     48.1   \n",
       "7              Agree     Agree in Proviso            Agree     38.8   \n",
       "8   Agree in Proviso     Agree in Proviso            Agree     39.9   \n",
       "9              Agree                Agree         Disagree     34.9   \n",
       "10          Disagree             Disagree         Disagree     57.3   \n",
       "11          Disagree             Disagree            Agree     43.2   \n",
       "12             Agree     Agree in Proviso            Agree     50.0   \n",
       "13             Agree                Agree            Agree     68.3   \n",
       "14  Agree in Proviso     Agree in Proviso            Agree     41.7   \n",
       "15             Agree                Agree            Agree     62.6   \n",
       "16  Agree in Proviso     Agree in Proviso            Agree     63.4   \n",
       "17          Disagree             Disagree            Agree     66.6   \n",
       "18  Agree in Proviso     Agree in Proviso            Agree     36.9   \n",
       "19             Agree                Agree            Agree     65.2   \n",
       "20          Disagree             Disagree         Disagree     61.6   \n",
       "21             Agree                Agree            Agree     60.1   \n",
       "22  Agree in Proviso     Agree in Proviso            Agree     43.7   \n",
       "23          Disagree             Disagree         Disagree     77.7   \n",
       "24             Agree                Agree            Agree     77.6   \n",
       "25  Agree in Proviso     Agree in Proviso            Agree     64.0   \n",
       "26             Agree                Agree            Agree     71.7   \n",
       "27          Disagree     Agree in Proviso         Disagree     35.1   \n",
       "28          Disagree             Disagree         Disagree     45.9   \n",
       "29          Disagree             Disagree         Disagree     53.5   \n",
       "30             Agree                Agree            Agree     60.0   \n",
       "31          Disagree             Disagree         Disagree     63.8   \n",
       "32             Agree                Agree            Agree     73.1   \n",
       "33             Agree                Agree            Agree     67.8   \n",
       "34  Agree in Proviso     Agree in Proviso            Agree     53.5   \n",
       "35  Agree in Proviso             Disagree            Agree     58.6   \n",
       "36          Disagree             Disagree         Disagree     74.0   \n",
       "37  Agree in Proviso     Agree in Proviso            Agree     48.7   \n",
       "38  Agree in Proviso     Agree in Proviso            Agree     39.4   \n",
       "39  Agree in Proviso     Agree in Proviso            Agree     64.1   \n",
       "40             Agree                Agree            Agree     64.8   \n",
       "41             Agree                Agree            Agree     54.1   \n",
       "42             Agree                Agree            Agree     62.6   \n",
       "43  Agree in Proviso     Agree in Proviso            Agree     45.3   \n",
       "44             Agree     Agree in Proviso            Agree     68.6   \n",
       "45  Agree in Proviso     Agree in Proviso            Agree     52.3   \n",
       "\n",
       "   ChatGPT4o MaxValue ChatGPT4oProf MaxValue ChatGPT35 MaxValue  \n",
       "0                99.5                   98.5               96.5  \n",
       "1                96.5                  100.0               71.0  \n",
       "2                51.5                   90.5               96.0  \n",
       "3                82.0                   52.5               54.0  \n",
       "4               100.0                  100.0               79.0  \n",
       "5                76.0                   98.5               99.5  \n",
       "6                95.5                   99.5               63.5  \n",
       "7                88.0                   50.5               95.0  \n",
       "8                93.5                   92.0               72.0  \n",
       "9                90.5                   80.5               97.0  \n",
       "10               56.0                   74.0               59.5  \n",
       "11               87.0                   96.5               41.0  \n",
       "12               57.5                   96.0               61.0  \n",
       "13               82.0                   83.5               91.5  \n",
       "14               97.5                  100.0               99.5  \n",
       "15               60.5                   57.5              100.0  \n",
       "16               90.0                   83.5              100.0  \n",
       "17               82.5                   72.5               47.0  \n",
       "18               96.0                   89.5               73.0  \n",
       "19               97.5                   55.5               99.5  \n",
       "20              100.0                   99.5               84.5  \n",
       "21               98.5                   86.0               99.5  \n",
       "22               78.5                   96.0               95.0  \n",
       "23              100.0                  100.0              100.0  \n",
       "24               99.5                   97.0              100.0  \n",
       "25               95.5                   93.0               97.5  \n",
       "26              100.0                  100.0              100.0  \n",
       "27               67.5                   99.0               81.5  \n",
       "28               95.0                   81.0               74.0  \n",
       "29               94.5                   90.0               80.5  \n",
       "30               79.5                   87.0               88.5  \n",
       "31               99.0                   95.5               91.5  \n",
       "32               96.0                   82.0               92.5  \n",
       "33               97.0                   71.0               99.5  \n",
       "34               99.0                  100.0               89.5  \n",
       "35               52.5                   96.5               91.0  \n",
       "36              100.0                  100.0               97.5  \n",
       "37               86.5                   95.0               95.5  \n",
       "38               81.0                   79.0               96.0  \n",
       "39               97.5                   95.5               94.0  \n",
       "40               97.5                   97.5              100.0  \n",
       "41               99.5                   94.0               97.5  \n",
       "42               98.0                   76.0               95.0  \n",
       "43               73.5                   95.5              100.0  \n",
       "44               69.0                   75.5               98.5  \n",
       "45               94.0                   78.5               67.0  \n",
       "\n",
       "[46 rows x 27 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltables['Median']= None\n",
    "alltables['ChatGPT4o Median']= None\n",
    "alltables['ChatGPT4oProf Median']= None\n",
    "alltables['ChatGPT35 Median']= None\n",
    "\n",
    "alltables['MaxValue']= None\n",
    "alltables['ChatGPT4o MaxValue']= None\n",
    "alltables['ChatGPT4oProf MaxValue']= None\n",
    "alltables['ChatGPT35 MaxValue']= None\n",
    "\n",
    "for i in range(0,46):\n",
    "    alltables.at[i,'Median']=alltables[['Disagree', 'Agree in Proviso', 'Agree']].iloc[i].to_frame().sort_values(i).index[2]\n",
    "    alltables.at[i,'ChatGPT4o Median']=alltables[['ChatGPT4o Disagree', 'ChatGPT4o Agree in Proviso', 'ChatGPT4o Agree']].iloc[i].to_frame().sort_values(i).index[2].replace('ChatGPT4o','').strip()\n",
    "    alltables.at[i,'ChatGPT4oProf Median']=alltables[['ChatGPT4oProf Disagree', 'ChatGPT4oProf Agree in Proviso', 'ChatGPT4oProf Agree']].iloc[i].to_frame().sort_values(i).index[2].replace('ChatGPT4oProf','').strip()\n",
    "    alltables.at[i,'ChatGPT35 Median']=alltables[['ChatGPT35 Disagree', 'ChatGPT35 Agree in Proviso', 'ChatGPT35 Agree']].iloc[i].to_frame().sort_values(i).index[2].replace('ChatGPT35','').strip()\n",
    "\n",
    "    alltables.at[i,'MaxValue']=alltables[['Disagree', 'Agree in Proviso', 'Agree']].iloc[i].to_frame().sort_values(i).max().to_list()[0]\n",
    "    alltables.at[i,'ChatGPT4o MaxValue']=alltables[['ChatGPT4o Disagree', 'ChatGPT4o Agree in Proviso', 'ChatGPT4o Agree']].iloc[i].to_frame().sort_values(i).max().to_list()[0]\n",
    "    alltables.at[i,'ChatGPT4oProf MaxValue']=alltables[['ChatGPT4oProf Disagree', 'ChatGPT4oProf Agree in Proviso', 'ChatGPT4oProf Agree']].iloc[i].to_frame().sort_values(i).max().to_list()[0]\n",
    "    alltables.at[i,'ChatGPT35 MaxValue']=alltables[['ChatGPT35 Disagree', 'ChatGPT35 Agree in Proviso', 'ChatGPT35 Agree']].iloc[i].to_frame().sort_values(i).max().to_list()[0]\n",
    "\n",
    "\n",
    "alltables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "0b036bad-11fc-4842-bd6f-87d2dde7ae16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  text-align: center;\n",
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       "  width: 300px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_31ac5\">\n",
       "  <caption>Table 6 - Distribution of Answers - AEA Members Survey vs. ChatGPT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_31ac5_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">Economists Survey</th>\n",
       "      <th id=\"T_31ac5_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">ChatGPT 3.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_31ac5_level1_col0\" class=\"col_heading level1 col0\" >Agree</th>\n",
       "      <th id=\"T_31ac5_level1_col1\" class=\"col_heading level1 col1\" >Agree in Proviso</th>\n",
       "      <th id=\"T_31ac5_level1_col2\" class=\"col_heading level1 col2\" >Disagree</th>\n",
       "      <th id=\"T_31ac5_level1_col3\" class=\"col_heading level1 col3\" >Agree</th>\n",
       "      <th id=\"T_31ac5_level1_col4\" class=\"col_heading level1 col4\" >Agree in Proviso</th>\n",
       "      <th id=\"T_31ac5_level1_col5\" class=\"col_heading level1 col5\" >Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row0\" class=\"row_heading level0 row0\" >mean</th>\n",
       "      <td id=\"T_31ac5_row0_col0\" class=\"data row0 col0 false \" >37.5</td>\n",
       "      <td id=\"T_31ac5_row0_col1\" class=\"data row0 col1 false \" >27.8</td>\n",
       "      <td id=\"T_31ac5_row0_col2\" class=\"data row0 col2 true \" >34.7</td>\n",
       "      <td id=\"T_31ac5_row0_col3\" class=\"data row0 col3\" >71.4</td>\n",
       "      <td id=\"T_31ac5_row0_col4\" class=\"data row0 col4\" >4.0</td>\n",
       "      <td id=\"T_31ac5_row0_col5\" class=\"data row0 col5\" >24.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row1\" class=\"row_heading level0 row1\" >min</th>\n",
       "      <td id=\"T_31ac5_row1_col0\" class=\"data row1 col0 false \" >6.1</td>\n",
       "      <td id=\"T_31ac5_row1_col1\" class=\"data row1 col1 false \" >14.3</td>\n",
       "      <td id=\"T_31ac5_row1_col2\" class=\"data row1 col2 true \" >2.4</td>\n",
       "      <td id=\"T_31ac5_row1_col3\" class=\"data row1 col3\" >0.0</td>\n",
       "      <td id=\"T_31ac5_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_31ac5_row1_col5\" class=\"data row1 col5\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row2\" class=\"row_heading level0 row2\" >25%</th>\n",
       "      <td id=\"T_31ac5_row2_col0\" class=\"data row2 col0 false \" >17.6</td>\n",
       "      <td id=\"T_31ac5_row2_col1\" class=\"data row2 col1 false \" >21.5</td>\n",
       "      <td id=\"T_31ac5_row2_col2\" class=\"data row2 col2 true \" >12.6</td>\n",
       "      <td id=\"T_31ac5_row2_col3\" class=\"data row2 col3\" >48.8</td>\n",
       "      <td id=\"T_31ac5_row2_col4\" class=\"data row2 col4\" >0.0</td>\n",
       "      <td id=\"T_31ac5_row2_col5\" class=\"data row2 col5\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row3\" class=\"row_heading level0 row3\" >50%</th>\n",
       "      <td id=\"T_31ac5_row3_col0\" class=\"data row3 col0 false \" >32.8</td>\n",
       "      <td id=\"T_31ac5_row3_col1\" class=\"data row3 col1 false \" >25.9</td>\n",
       "      <td id=\"T_31ac5_row3_col2\" class=\"data row3 col2 true \" >35.0</td>\n",
       "      <td id=\"T_31ac5_row3_col3\" class=\"data row3 col3\" >92.0</td>\n",
       "      <td id=\"T_31ac5_row3_col4\" class=\"data row3 col4\" >1.8</td>\n",
       "      <td id=\"T_31ac5_row3_col5\" class=\"data row3 col5\" >3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row4\" class=\"row_heading level0 row4\" >75%</th>\n",
       "      <td id=\"T_31ac5_row4_col0\" class=\"data row4 col0 false \" >63.2</td>\n",
       "      <td id=\"T_31ac5_row4_col1\" class=\"data row4 col1 false \" >33.5</td>\n",
       "      <td id=\"T_31ac5_row4_col2\" class=\"data row4 col2 true \" >53.2</td>\n",
       "      <td id=\"T_31ac5_row4_col3\" class=\"data row4 col3\" >98.2</td>\n",
       "      <td id=\"T_31ac5_row4_col4\" class=\"data row4 col4\" >4.8</td>\n",
       "      <td id=\"T_31ac5_row4_col5\" class=\"data row4 col5\" >39.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31ac5_level0_row5\" class=\"row_heading level0 row5\" >max</th>\n",
       "      <td id=\"T_31ac5_row5_col0\" class=\"data row5 col0 false \" >77.6</td>\n",
       "      <td id=\"T_31ac5_row5_col1\" class=\"data row5 col1 false \" >45.3</td>\n",
       "      <td id=\"T_31ac5_row5_col2\" class=\"data row5 col2 true \" >77.7</td>\n",
       "      <td id=\"T_31ac5_row5_col3\" class=\"data row5 col3\" >100.0</td>\n",
       "      <td id=\"T_31ac5_row5_col4\" class=\"data row5 col4\" >26.0</td>\n",
       "      <td id=\"T_31ac5_row5_col5\" class=\"data row5 col5\" >100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd5db79130>"
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     },
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    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "  font-size: 150%;\n",
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       "}\n",
       "#T_31d52 td {\n",
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       "#T_31d52 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_31d52 .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_31d52_row0_col0, #T_31d52_row0_col1, #T_31d52_row0_col2, #T_31d52_row0_col3, #T_31d52_row0_col4, #T_31d52_row0_col5, #T_31d52_row1_col0, #T_31d52_row1_col1, #T_31d52_row1_col2, #T_31d52_row1_col3, #T_31d52_row1_col4, #T_31d52_row1_col5, #T_31d52_row2_col0, #T_31d52_row2_col1, #T_31d52_row2_col2, #T_31d52_row2_col3, #T_31d52_row2_col4, #T_31d52_row2_col5, #T_31d52_row3_col0, #T_31d52_row3_col1, #T_31d52_row3_col2, #T_31d52_row3_col3, #T_31d52_row3_col4, #T_31d52_row3_col5, #T_31d52_row4_col0, #T_31d52_row4_col1, #T_31d52_row4_col2, #T_31d52_row4_col3, #T_31d52_row4_col4, #T_31d52_row4_col5, #T_31d52_row5_col0, #T_31d52_row5_col1, #T_31d52_row5_col2, #T_31d52_row5_col3, #T_31d52_row5_col4, #T_31d52_row5_col5 {\n",
       "  width: 300px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_31d52\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_31d52_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">ChatGPT 4o</th>\n",
       "      <th id=\"T_31d52_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">ChatGPT 4oProf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_31d52_level1_col0\" class=\"col_heading level1 col0\" >Agree</th>\n",
       "      <th id=\"T_31d52_level1_col1\" class=\"col_heading level1 col1\" >Agree in Proviso</th>\n",
       "      <th id=\"T_31d52_level1_col2\" class=\"col_heading level1 col2\" >Disagree</th>\n",
       "      <th id=\"T_31d52_level1_col3\" class=\"col_heading level1 col3\" >Agree</th>\n",
       "      <th id=\"T_31d52_level1_col4\" class=\"col_heading level1 col4\" >Agree in Proviso</th>\n",
       "      <th id=\"T_31d52_level1_col5\" class=\"col_heading level1 col5\" >Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row0\" class=\"row_heading level0 row0\" >mean</th>\n",
       "      <td id=\"T_31d52_row0_col0\" class=\"data row0 col0 false \" >36.7</td>\n",
       "      <td id=\"T_31d52_row0_col1\" class=\"data row0 col1 false \" >38.8</td>\n",
       "      <td id=\"T_31d52_row0_col2\" class=\"data row0 col2 true \" >24.5</td>\n",
       "      <td id=\"T_31d52_row0_col3\" class=\"data row0 col3\" >28.6</td>\n",
       "      <td id=\"T_31d52_row0_col4\" class=\"data row0 col4\" >47.5</td>\n",
       "      <td id=\"T_31d52_row0_col5\" class=\"data row0 col5\" >23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row1\" class=\"row_heading level0 row1\" >min</th>\n",
       "      <td id=\"T_31d52_row1_col0\" class=\"data row1 col0 false \" >0.0</td>\n",
       "      <td id=\"T_31d52_row1_col1\" class=\"data row1 col1 false \" >0.0</td>\n",
       "      <td id=\"T_31d52_row1_col2\" class=\"data row1 col2 true \" >0.0</td>\n",
       "      <td id=\"T_31d52_row1_col3\" class=\"data row1 col3\" >0.0</td>\n",
       "      <td id=\"T_31d52_row1_col4\" class=\"data row1 col4\" >0.0</td>\n",
       "      <td id=\"T_31d52_row1_col5\" class=\"data row1 col5\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row2\" class=\"row_heading level0 row2\" >25%</th>\n",
       "      <td id=\"T_31d52_row2_col0\" class=\"data row2 col0 false \" >0.0</td>\n",
       "      <td id=\"T_31d52_row2_col1\" class=\"data row2 col1 false \" >2.6</td>\n",
       "      <td id=\"T_31d52_row2_col2\" class=\"data row2 col2 true \" >0.0</td>\n",
       "      <td id=\"T_31d52_row2_col3\" class=\"data row2 col3\" >0.0</td>\n",
       "      <td id=\"T_31d52_row2_col4\" class=\"data row2 col4\" >7.0</td>\n",
       "      <td id=\"T_31d52_row2_col5\" class=\"data row2 col5\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row3\" class=\"row_heading level0 row3\" >50%</th>\n",
       "      <td id=\"T_31d52_row3_col0\" class=\"data row3 col0 false \" >8.2</td>\n",
       "      <td id=\"T_31d52_row3_col1\" class=\"data row3 col1 false \" >19.2</td>\n",
       "      <td id=\"T_31d52_row3_col2\" class=\"data row3 col2 true \" >0.0</td>\n",
       "      <td id=\"T_31d52_row3_col3\" class=\"data row3 col3\" >4.0</td>\n",
       "      <td id=\"T_31d52_row3_col4\" class=\"data row3 col4\" >35.8</td>\n",
       "      <td id=\"T_31d52_row3_col5\" class=\"data row3 col5\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row4\" class=\"row_heading level0 row4\" >75%</th>\n",
       "      <td id=\"T_31d52_row4_col0\" class=\"data row4 col0 false \" >86.5</td>\n",
       "      <td id=\"T_31d52_row4_col1\" class=\"data row4 col1 false \" >80.4</td>\n",
       "      <td id=\"T_31d52_row4_col2\" class=\"data row4 col2 true \" >52.8</td>\n",
       "      <td id=\"T_31d52_row4_col3\" class=\"data row4 col3\" >67.6</td>\n",
       "      <td id=\"T_31d52_row4_col4\" class=\"data row4 col4\" >92.8</td>\n",
       "      <td id=\"T_31d52_row4_col5\" class=\"data row4 col5\" >40.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_31d52_level0_row5\" class=\"row_heading level0 row5\" >max</th>\n",
       "      <td id=\"T_31d52_row5_col0\" class=\"data row5 col0 false \" >100.0</td>\n",
       "      <td id=\"T_31d52_row5_col1\" class=\"data row5 col1 false \" >99.5</td>\n",
       "      <td id=\"T_31d52_row5_col2\" class=\"data row5 col2 true \" >100.0</td>\n",
       "      <td id=\"T_31d52_row5_col3\" class=\"data row5 col3\" >100.0</td>\n",
       "      <td id=\"T_31d52_row5_col4\" class=\"data row5 col4\" >100.0</td>\n",
       "      <td id=\"T_31d52_row5_col5\" class=\"data row5 col5\" >100.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcd5d05fa00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on 46 questions from Geide-Stevenson and Alvaro La Parra Perez (forthcoming). \n",
      "The Economists Survey panel3 (top left) gives the distribution of the share of respondents who chose a given answer, across questions. \n",
      "The other panels give the distribution of ChatGPT answers, when querried 200 times, across questions.\n"
     ]
    }
   ],
   "source": [
    "a=alltables[['Agree','Agree in Proviso', 'Disagree']].describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "b=alltables[['ChatGPT35 Agree','ChatGPT35 Agree in Proviso', 'ChatGPT35 Disagree']].astype(float).describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "\n",
    "\n",
    "clean=a.columns\n",
    "\n",
    "b.columns=clean\n",
    "a=pd.concat([a], keys=['Economists Survey'], axis=1)\n",
    "b=pd.concat([b], keys=['ChatGPT 3.5'], axis=1)\n",
    "\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([[ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true ']],\n",
    "                          index=a.index,\n",
    "                          columns=a.columns)\n",
    "\n",
    "\n",
    "\n",
    "display(pd.concat([a,b], axis=1).style.format(precision=1).set_properties(**{'width': '300px'}).set_caption(\"Table 6 - Distribution of Answers - AEA Members Survey vs. ChatGPT\").set_table_styles(styles).set_td_classes(cell_color))\n",
    "\n",
    "c=alltables[['ChatGPT4o Agree','ChatGPT4o Agree in Proviso', 'ChatGPT4o Disagree']].astype(float).describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "d=alltables[['ChatGPT4oProf Agree','ChatGPT4oProf Agree in Proviso', 'ChatGPT4oProf Disagree']].astype(float).describe().loc[['mean','min','25%','50%', '75%','max']]\n",
    "\n",
    "c.columns=clean\n",
    "d.columns=clean\n",
    "c=pd.concat([c], keys=['ChatGPT 4o'], axis=1)\n",
    "d=pd.concat([d], keys=['ChatGPT 4oProf'], axis=1)\n",
    "\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([[ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true '],\n",
    "                           [ 'false ', 'false ', 'true ']],\n",
    "                          index=c.index,\n",
    "                          columns=c.columns)\n",
    "\n",
    "\n",
    "\n",
    "display(pd.concat([c,d], axis=1).style.format(precision=1).set_properties(**{'width': '300px'}).set_table_styles(styles).set_td_classes(cell_color))\n",
    "print('Notes: this table is based on 46 questions from Geide-Stevenson and Alvaro La Parra Perez (forthcoming). \\nThe Economists Survey panel3 (top left) gives the distribution of the share of respondents who chose a given answer, across questions. \\nThe other panels give the distribution of ChatGPT answers, when querried 200 times, across questions.')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "e96fc87e-90d2-490f-bef0-f27cbac5accf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: blue;\n",
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       "  width: 100px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d3d0c\">\n",
       "  <caption>Table 7 - AEA Members vs. ChatGPT</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d3d0c_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">ChatGPT 3.5</th>\n",
       "      <th id=\"T_d3d0c_level0_col3\" class=\"col_heading level0 col3\" colspan=\"3\">ChatGPT 4o</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_d3d0c_level1_col0\" class=\"col_heading level1 col0\" >Agree</th>\n",
       "      <th id=\"T_d3d0c_level1_col1\" class=\"col_heading level1 col1\" >Agree in Proviso</th>\n",
       "      <th id=\"T_d3d0c_level1_col2\" class=\"col_heading level1 col2\" >Disagree</th>\n",
       "      <th id=\"T_d3d0c_level1_col3\" class=\"col_heading level1 col3\" >Agree</th>\n",
       "      <th id=\"T_d3d0c_level1_col4\" class=\"col_heading level1 col4\" >Agree in Proviso</th>\n",
       "      <th id=\"T_d3d0c_level1_col5\" class=\"col_heading level1 col5\" >Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d3d0c_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Economists</th>\n",
       "      <th id=\"T_d3d0c_level1_row0\" class=\"row_heading level1 row0\" >Agree</th>\n",
       "      <td id=\"T_d3d0c_row0_col0\" class=\"data row0 col0 true \" >17</td>\n",
       "      <td id=\"T_d3d0c_row0_col1\" class=\"data row0 col1 false \" >0</td>\n",
       "      <td id=\"T_d3d0c_row0_col2\" class=\"data row0 col2 true2 \" >0</td>\n",
       "      <td id=\"T_d3d0c_row0_col3\" class=\"data row0 col3 true \" >14</td>\n",
       "      <td id=\"T_d3d0c_row0_col4\" class=\"data row0 col4 false \" >3</td>\n",
       "      <td id=\"T_d3d0c_row0_col5\" class=\"data row0 col5 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d3d0c_level1_row1\" class=\"row_heading level1 row1\" >Agree in Proviso</th>\n",
       "      <td id=\"T_d3d0c_row1_col0\" class=\"data row1 col0 false\" >7</td>\n",
       "      <td id=\"T_d3d0c_row1_col1\" class=\"data row1 col1 true \" >0</td>\n",
       "      <td id=\"T_d3d0c_row1_col2\" class=\"data row1 col2 true2 \" >1</td>\n",
       "      <td id=\"T_d3d0c_row1_col3\" class=\"data row1 col3 false\" >2</td>\n",
       "      <td id=\"T_d3d0c_row1_col4\" class=\"data row1 col4 true \" >5</td>\n",
       "      <td id=\"T_d3d0c_row1_col5\" class=\"data row1 col5 false \" >1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d3d0c_level1_row2\" class=\"row_heading level1 row2\" >Disagree</th>\n",
       "      <td id=\"T_d3d0c_row2_col0\" class=\"data row2 col0 false \" >12</td>\n",
       "      <td id=\"T_d3d0c_row2_col1\" class=\"data row2 col1 false \" >0</td>\n",
       "      <td id=\"T_d3d0c_row2_col2\" class=\"data row2 col2 true3 \" >9</td>\n",
       "      <td id=\"T_d3d0c_row2_col3\" class=\"data row2 col3 false \" >2</td>\n",
       "      <td id=\"T_d3d0c_row2_col4\" class=\"data row2 col4 false \" >8</td>\n",
       "      <td id=\"T_d3d0c_row2_col5\" class=\"data row2 col5 true \" >11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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      "\n"
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     "data": {
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       "<style type=\"text/css\">\n",
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       "  text-align: center;\n",
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       "}\n",
       "#T_084a0 td {\n",
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       "  text-align: center;\n",
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       "#T_084a0 .true {\n",
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       "#T_084a0 .true2 {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_084a0 .true3 {\n",
       "  color: blue;\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_084a0_row0_col0, #T_084a0_row0_col1, #T_084a0_row0_col2, #T_084a0_row1_col0, #T_084a0_row1_col1, #T_084a0_row1_col2, #T_084a0_row2_col0, #T_084a0_row2_col1, #T_084a0_row2_col2 {\n",
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       "</style>\n",
       "<table id=\"T_084a0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_084a0_level0_col0\" class=\"col_heading level0 col0\" colspan=\"3\">ChatGPT4oProf</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_084a0_level1_col0\" class=\"col_heading level1 col0\" >Agree</th>\n",
       "      <th id=\"T_084a0_level1_col1\" class=\"col_heading level1 col1\" >Agree in Proviso</th>\n",
       "      <th id=\"T_084a0_level1_col2\" class=\"col_heading level1 col2\" >Disagree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_084a0_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"3\">Economists</th>\n",
       "      <th id=\"T_084a0_level1_row0\" class=\"row_heading level1 row0\" >Agree</th>\n",
       "      <td id=\"T_084a0_row0_col0\" class=\"data row0 col0 true \" >13</td>\n",
       "      <td id=\"T_084a0_row0_col1\" class=\"data row0 col1 false \" >4</td>\n",
       "      <td id=\"T_084a0_row0_col2\" class=\"data row0 col2 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_084a0_level1_row1\" class=\"row_heading level1 row1\" >Agree in Proviso</th>\n",
       "      <td id=\"T_084a0_row1_col0\" class=\"data row1 col0 false\" >0</td>\n",
       "      <td id=\"T_084a0_row1_col1\" class=\"data row1 col1 true \" >8</td>\n",
       "      <td id=\"T_084a0_row1_col2\" class=\"data row1 col2 false \" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_084a0_level1_row2\" class=\"row_heading level1 row2\" >Disagree</th>\n",
       "      <td id=\"T_084a0_row2_col0\" class=\"data row2 col0 false \" >1</td>\n",
       "      <td id=\"T_084a0_row2_col1\" class=\"data row2 col1 false \" >9</td>\n",
       "      <td id=\"T_084a0_row2_col2\" class=\"data row2 col2 true\" >11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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     "metadata": {},
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on 46 questions from Geide-Stevenson and Alvaro La Parra Perez (forthcoming). \n",
      "The Economics Profs Survey panel (top left) gives the distribution of the share of respondents who chose a given answer, across questions. \n",
      "The other panels give the distribution of answers of various versions of ChatGPT, when querried 200 times, across questions.\n"
     ]
    }
   ],
   "source": [
    "byquestioncross=pd.DataFrame(0,index=['Agree', 'Agree in Proviso', 'Disagree'], columns=['Agree', 'Agree in Proviso', 'Disagree'])\n",
    "\n",
    "for i in range(0,46):\n",
    "    byquestioncross.at[alltables['Median'][i],alltables['ChatGPT35 Median'][i]]=byquestioncross.at[alltables['Median'][i],alltables['ChatGPT35 Median'][i]]+1\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economists'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 3.5'], axis=1)\n",
    "\n",
    "byquestioncross35=byquestioncross\n",
    "\n",
    "byquestioncross=pd.DataFrame(0,index=['Agree', 'Agree in Proviso', 'Disagree'], columns=['Agree', 'Agree in Proviso', 'Disagree'])\n",
    "\n",
    "for i in range(0,46):\n",
    "    byquestioncross.at[alltables['Median'][i],alltables['ChatGPT4o Median'][i]]=byquestioncross.at[alltables['Median'][i],alltables['ChatGPT4o Median'][i]]+1\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economists'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT 4o'], axis=1)\n",
    "\n",
    "byquestioncross4o=byquestioncross\n",
    "\n",
    "byquestioncross=pd.DataFrame(0,index=['Agree', 'Agree in Proviso', 'Disagree'], columns=['Agree', 'Agree in Proviso', 'Disagree'])\n",
    "\n",
    "for i in range(0,46):\n",
    "    byquestioncross.at[alltables['Median'][i],alltables['ChatGPT4oProf Median'][i]]=byquestioncross.at[alltables['Median'][i],alltables['ChatGPT4oProf Median'][i]]+1\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['Economists'])\n",
    "byquestioncross=pd.concat([byquestioncross], keys=['ChatGPT4oProf'], axis=1)\n",
    "\n",
    "byquestioncross4oProf=byquestioncross\n",
    "\n",
    "a=pd.concat([byquestioncross35,byquestioncross4o], axis=1)\n",
    "# styles for tables\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'},\n",
    "        {'selector': '.true2', 'props': 'border-right:solid; border-color:blue; vertical-align:top'},\n",
    "         {'selector': '.true3', 'props': 'color: blue;border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "\n",
    "cell_color = pd.DataFrame([['true ', 'false ', 'true2 ','true ', 'false ', 'false '],\n",
    "                           ['false', 'true ',  'true2 ','false', 'true ', 'false '],\n",
    "                           ['false ', 'false ', 'true3 ', 'false ', 'false ', 'true ']],\n",
    "                          index=a.index,\n",
    "                          columns=a.columns)\n",
    "\n",
    "\n",
    "display(a.style.set_caption(\"Table 7 - AEA Members vs. ChatGPT\").set_properties(**{'width': '100px'}).set_table_styles(styles).set_td_classes(cell_color))\n",
    "\n",
    "cell_color = pd.DataFrame([['true ', 'false ', 'false '],\n",
    "                           ['false', 'true ', 'false '],\n",
    "                           ['false ', 'false ', 'true' ]],\n",
    "                          index=byquestioncross4oProf.index,\n",
    "                          columns=byquestioncross4oProf.columns)\n",
    "                          \n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center; width: 68px'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'},\n",
    "        {'selector': '.true2', 'props': 'border-right:solid; border-color:blue; vertical-align:top'},\n",
    "         {'selector': '.true3', 'props': 'color: blue;border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "print(\"\")\n",
    "display(byquestioncross4oProf.style.set_table_styles(styles).set_td_classes(cell_color).set_properties(**{'width': '120px'}))\n",
    "print('Notes: this table is based on 46 questions from Geide-Stevenson and Alvaro La Parra Perez (forthcoming). \\nThe Economics Profs Survey panel (top left) gives the distribution of the share of respondents who chose a given answer, across questions. \\nThe other panels give the distribution of answers of various versions of ChatGPT, when querried 200 times, across questions.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f2f61d66-60ac-4aa5-b49b-64a9ca1128bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding the chance to get consensus\n",
    "a1=pd.DataFrame(columns=['Agree', 'Agree in Proviso','Disagree'])\n",
    "a1.at[0,'Agree']=1\n",
    "a1.at[0,'Agree in Proviso']=2\n",
    "a1.at[0,'Disagree']=3\n",
    "a1\n",
    "\n",
    "# adding chance of getting the consensus when one \n",
    "for j in range(0, len(alltables)):\n",
    "#for j in range(0, 1):\n",
    "    alltables.at[j,'Chance Top Prof Choice']=alltables[alltables['Median'][j]][j]\n",
    "    alltables.at[j,'Chance GPT4o Top Prof Choice']=alltables['ChatGPT4o ' +alltables['Median'][j]][j]\n",
    "    alltables.at[j,'Chance GPT35 Top Prof Choice']=alltables['ChatGPT35 ' +alltables['Median'][j]][j]\n",
    "    alltables.at[j,'Chance GPT4oProf Top Prof Choice']=alltables['ChatGPT4oProf ' +alltables['Median'][j]][j]\n",
    "        \n",
    "    b=a1.loc[0]==a1[alltables['Median'][j]].to_list()[0]+1\n",
    "    \n",
    "    try:\n",
    "        b1=b[b==True].idxmax()\n",
    "    except:\n",
    "        b1='to be removed'\n",
    "    \n",
    "    b=a1.loc[0]==a1[alltables['Median'][j]].to_list()[0]-1\n",
    "    \n",
    "    try:\n",
    "        b2=b[b==True].idxmax()\n",
    "    except:\n",
    "        b2='to be removed'\n",
    "    b=[b1,b2]\n",
    "    c=[]\n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=['ChatGPT4o ' + i]\n",
    "    \n",
    "    alltables.at[j,'Chance GPT4o One Off Top Prof Choice']=np.sum(alltables[c].loc[j])\n",
    "    alltables.at[j,'Chance GPT4o More than One Off Top Prof Choice']=100-alltables.at[j,'Chance GPT4o One Off Top Prof Choice']-alltables.at[j,'Chance GPT4o Top Prof Choice']\n",
    "   \n",
    "    c=[]\n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=['ChatGPT4oProf ' + i]\n",
    "    alltables.at[j,'Chance GPT4oProf One Off Top Prof Choice']=np.sum(alltables[c].loc[j])\n",
    "    alltables.at[j,'Chance GPT4oProf More than One Off Top Prof Choice']=100-alltables.at[j,'Chance GPT4oProf One Off Top Prof Choice']-alltables.at[j,'Chance GPT4oProf Top Prof Choice']\n",
    "\n",
    "    c=[]\n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=['ChatGPT35 ' + i]\n",
    "\n",
    "    alltables.at[j,'Chance GPT35 One Off Top Prof Choice']=np.sum(alltables[c].loc[j])\n",
    "    alltables.at[j,'Chance GPT35 More than One Off Top Prof Choice']=100-alltables.at[j,'Chance GPT35 One Off Top Prof Choice']-alltables.at[j,'Chance GPT35 Top Prof Choice']\n",
    "\n",
    "    c=[]\n",
    "    for i in b:\n",
    "        if not 'to be removed' in i:\n",
    "            c+=[i]\n",
    "\n",
    "    alltables.at[j,'Chance One Off Top Prof Choice']=np.sum(alltables[c].loc[j])\n",
    "    alltables.at[j,'Chance More than One Off Top Prof Choice']=100-alltables.at[j,'Chance One Off Top Prof Choice']-alltables.at[j,'Chance Top Prof Choice']\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3113dec5-8e9d-43e0-8e2c-3ed7ae96ada5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<style type=\"text/css\">\n",
       "#T_a8291 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_a8291 td {\n",
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       "#T_a8291 th {\n",
       "  text-align: center;\n",
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       "#T_a8291 .true {\n",
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       "#T_a8291 .true2 {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "#T_a8291 .true3 {\n",
       "  color: blue;\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a8291\">\n",
       "  <caption>Table 8 - AEA Members vs ChatGPT : When Asked On(c)e, Chance You Get ..., in %</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a8291_level0_col0\" class=\"col_heading level0 col0\" >Most Common Answer</th>\n",
       "      <th id=\"T_a8291_level0_col1\" class=\"col_heading level0 col1\" >One Category Off Most Common Answer</th>\n",
       "      <th id=\"T_a8291_level0_col2\" class=\"col_heading level0 col2\" >More Than One Category Off Most Common Answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a8291_level0_row0\" class=\"row_heading level0 row0\" >Economists Survey</th>\n",
       "      <td id=\"T_a8291_row0_col0\" class=\"data row0 col0\" >57.0</td>\n",
       "      <td id=\"T_a8291_row0_col1\" class=\"data row0 col1\" >31.5</td>\n",
       "      <td id=\"T_a8291_row0_col2\" class=\"data row0 col2\" >11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8291_level0_row1\" class=\"row_heading level0 row1\" >GTP3.5</th>\n",
       "      <td id=\"T_a8291_row1_col0\" class=\"data row1 col0\" >56.6</td>\n",
       "      <td id=\"T_a8291_row1_col1\" class=\"data row1 col1\" >20.4</td>\n",
       "      <td id=\"T_a8291_row1_col2\" class=\"data row1 col2\" >22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8291_level0_row2\" class=\"row_heading level0 row2\" >GTP4o</th>\n",
       "      <td id=\"T_a8291_row2_col0\" class=\"data row2 col0\" >62.5</td>\n",
       "      <td id=\"T_a8291_row2_col1\" class=\"data row2 col1\" >32.9</td>\n",
       "      <td id=\"T_a8291_row2_col2\" class=\"data row2 col2\" >4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a8291_level0_row3\" class=\"row_heading level0 row3\" >GPT40Prof</th>\n",
       "      <td id=\"T_a8291_row3_col0\" class=\"data row3 col0\" >63.4</td>\n",
       "      <td id=\"T_a8291_row3_col1\" class=\"data row3 col1\" >34.6</td>\n",
       "      <td id=\"T_a8291_row3_col2\" class=\"data row3 col2\" >2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fcea1443d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on 60 Clark Center Survey questions. \n",
      "It gives the chance you get, on a specific question, the most common answer of the Economics Profs Survey, \n",
      "if you ask one Professor that question, or if you ask ChatGPT that question once\n"
     ]
    }
   ],
   "source": [
    "a=alltables[['Chance Top Prof Choice','Chance GPT35 Top Prof Choice','Chance GPT4o Top Prof Choice','Chance GPT4oProf Top Prof Choice']]\n",
    "a.columns=['Economists Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "a=a.describe().loc['mean']\n",
    "a=a.rename('Most Common Answer')\n",
    "\n",
    "b=alltables[['Chance One Off Top Prof Choice','Chance GPT35 One Off Top Prof Choice','Chance GPT4o One Off Top Prof Choice','Chance GPT4oProf One Off Top Prof Choice']]\n",
    "b.columns=['Economists Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "b=b.describe().loc['mean']\n",
    "b=b.rename('One Category Off Most Common Answer')\n",
    "\n",
    "c=alltables[['Chance More than One Off Top Prof Choice','Chance GPT35 More than One Off Top Prof Choice','Chance GPT4o More than One Off Top Prof Choice','Chance GPT4oProf More than One Off Top Prof Choice']]\n",
    "c.columns=['Economists Survey', 'GTP3.5', 'GTP4o', 'GPT40Prof']\n",
    "c=c.describe().loc['mean']\n",
    "c=c.rename('More Than One Category Off Most Common Answer')\n",
    "\n",
    "display(pd.concat([a,b,c], axis=1).style.set_caption(\"Table - AEA Members vs ChatGPT : When Asked On(c)e, Chance You Get ..., in %\").set_table_styles(styles).format(precision=1))\n",
    "print('Notes: this table is based on 60 Clark Center Survey questions. \\nIt gives the chance you get, on a specific question, the most common answer of the Economics Profs Survey, \\nif you ask one Professor that question, or if you ask ChatGPT that question once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "8b1927b2-f88a-464c-b0f5-3f4803c43929",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e46a8 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_e46a8 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_e46a8 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_e46a8 .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e46a8\">\n",
       "  <caption>Table A3 - Geide-Stevenson and Alvaro La Parra Perez (forthcoming)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_e46a8_level0_col0\" class=\"col_heading level0 col0\" >Claim</th>\n",
       "      <th id=\"T_e46a8_level0_col1\" class=\"col_heading level0 col1\" >Disagree</th>\n",
       "      <th id=\"T_e46a8_level0_col2\" class=\"col_heading level0 col2\" >Agree in Proviso</th>\n",
       "      <th id=\"T_e46a8_level0_col3\" class=\"col_heading level0 col3\" >Agree</th>\n",
       "      <th id=\"T_e46a8_level0_col4\" class=\"col_heading level0 col4\" >ChatGPT4o Disagree</th>\n",
       "      <th id=\"T_e46a8_level0_col5\" class=\"col_heading level0 col5\" >ChatGPT4o Agree in Proviso</th>\n",
       "      <th id=\"T_e46a8_level0_col6\" class=\"col_heading level0 col6\" >ChatGPT4o Agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row0_col0\" class=\"data row0 col0\" >Flexible and floating exchange rates offer an effective international monetary arrangement.</td>\n",
       "      <td id=\"T_e46a8_row0_col1\" class=\"data row0 col1\" >2.4</td>\n",
       "      <td id=\"T_e46a8_row0_col2\" class=\"data row0 col2\" >28.3</td>\n",
       "      <td id=\"T_e46a8_row0_col3\" class=\"data row0 col3\" >69.2</td>\n",
       "      <td id=\"T_e46a8_row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row0_col5\" class=\"data row0 col5\" >99.5</td>\n",
       "      <td id=\"T_e46a8_row0_col6\" class=\"data row0 col6\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row1_col0\" class=\"data row1 col0\" >Tariffs and import quotas usually reduce general economic welfare.</td>\n",
       "      <td id=\"T_e46a8_row1_col1\" class=\"data row1 col1\" >5.3</td>\n",
       "      <td id=\"T_e46a8_row1_col2\" class=\"data row1 col2\" >25.4</td>\n",
       "      <td id=\"T_e46a8_row1_col3\" class=\"data row1 col3\" >69.3</td>\n",
       "      <td id=\"T_e46a8_row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row1_col5\" class=\"data row1 col5\" >3.5</td>\n",
       "      <td id=\"T_e46a8_row1_col6\" class=\"data row1 col6\" >96.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row2_col0\" class=\"data row2 col0\" >Some restrictions on the flow of financial capital are essential to the stability and soundness of the international financial system.</td>\n",
       "      <td id=\"T_e46a8_row2_col1\" class=\"data row2 col1\" >24.6</td>\n",
       "      <td id=\"T_e46a8_row2_col2\" class=\"data row2 col2\" >39.8</td>\n",
       "      <td id=\"T_e46a8_row2_col3\" class=\"data row2 col3\" >35.6</td>\n",
       "      <td id=\"T_e46a8_row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row2_col5\" class=\"data row2 col5\" >48.5</td>\n",
       "      <td id=\"T_e46a8_row2_col6\" class=\"data row2 col6\" >51.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row3_col0\" class=\"data row3 col0\" >The economic benefits of an expanding world population outweigh the economic costs.</td>\n",
       "      <td id=\"T_e46a8_row3_col1\" class=\"data row3 col1\" >42.4</td>\n",
       "      <td id=\"T_e46a8_row3_col2\" class=\"data row3 col2\" >32.5</td>\n",
       "      <td id=\"T_e46a8_row3_col3\" class=\"data row3 col3\" >25.0</td>\n",
       "      <td id=\"T_e46a8_row3_col4\" class=\"data row3 col4\" >82.0</td>\n",
       "      <td id=\"T_e46a8_row3_col5\" class=\"data row3 col5\" >18.0</td>\n",
       "      <td id=\"T_e46a8_row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row4_col0\" class=\"data row4 col0\" >The persistent U.S. trade deficit is due primarily to non-tariff trade barriers and/or nominal exchange rate manipulations.</td>\n",
       "      <td id=\"T_e46a8_row4_col1\" class=\"data row4 col1\" >77.3</td>\n",
       "      <td id=\"T_e46a8_row4_col2\" class=\"data row4 col2\" >14.5</td>\n",
       "      <td id=\"T_e46a8_row4_col3\" class=\"data row4 col3\" >8.2</td>\n",
       "      <td id=\"T_e46a8_row4_col4\" class=\"data row4 col4\" >100.0</td>\n",
       "      <td id=\"T_e46a8_row4_col5\" class=\"data row4 col5\" >0</td>\n",
       "      <td id=\"T_e46a8_row4_col6\" class=\"data row4 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row5_col0\" class=\"data row5 col0\" >A large balance of trade deficit has an adverse effect on the economy.</td>\n",
       "      <td id=\"T_e46a8_row5_col1\" class=\"data row5 col1\" >65.2</td>\n",
       "      <td id=\"T_e46a8_row5_col2\" class=\"data row5 col2\" >25.9</td>\n",
       "      <td id=\"T_e46a8_row5_col3\" class=\"data row5 col3\" >8.9</td>\n",
       "      <td id=\"T_e46a8_row5_col4\" class=\"data row5 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row5_col5\" class=\"data row5 col5\" >76.0</td>\n",
       "      <td id=\"T_e46a8_row5_col6\" class=\"data row5 col6\" >24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row6_col0\" class=\"data row6 col0\" >An economy that operates below potential GDP has a self correcting mechanism that will eventually return it to potential GDP.</td>\n",
       "      <td id=\"T_e46a8_row6_col1\" class=\"data row6 col1\" >48.1</td>\n",
       "      <td id=\"T_e46a8_row6_col2\" class=\"data row6 col2\" >38.9</td>\n",
       "      <td id=\"T_e46a8_row6_col3\" class=\"data row6 col3\" >12.9</td>\n",
       "      <td id=\"T_e46a8_row6_col4\" class=\"data row6 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row6_col5\" class=\"data row6 col5\" >95.5</td>\n",
       "      <td id=\"T_e46a8_row6_col6\" class=\"data row6 col6\" >4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row7_col0\" class=\"data row7 col0\" >There is a natural rate of unemployment to which the economy tends in the long run.</td>\n",
       "      <td id=\"T_e46a8_row7_col1\" class=\"data row7 col1\" >26.0</td>\n",
       "      <td id=\"T_e46a8_row7_col2\" class=\"data row7 col2\" >38.8</td>\n",
       "      <td id=\"T_e46a8_row7_col3\" class=\"data row7 col3\" >35.2</td>\n",
       "      <td id=\"T_e46a8_row7_col4\" class=\"data row7 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row7_col5\" class=\"data row7 col5\" >12.0</td>\n",
       "      <td id=\"T_e46a8_row7_col6\" class=\"data row7 col6\" >88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row8_col0\" class=\"data row8 col0\" >The Federal Reserve has the capacity to achieve a constant rate of growth in the money supply if it so desired.</td>\n",
       "      <td id=\"T_e46a8_row8_col1\" class=\"data row8 col1\" >25.3</td>\n",
       "      <td id=\"T_e46a8_row8_col2\" class=\"data row8 col2\" >39.9</td>\n",
       "      <td id=\"T_e46a8_row8_col3\" class=\"data row8 col3\" >34.8</td>\n",
       "      <td id=\"T_e46a8_row8_col4\" class=\"data row8 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row8_col5\" class=\"data row8 col5\" >93.5</td>\n",
       "      <td id=\"T_e46a8_row8_col6\" class=\"data row8 col6\" >6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row9_col0\" class=\"data row9 col0\" >Changes in aggregate demand affect real GDP in the short run but not in the long run.</td>\n",
       "      <td id=\"T_e46a8_row9_col1\" class=\"data row9 col1\" >34.9</td>\n",
       "      <td id=\"T_e46a8_row9_col2\" class=\"data row9 col2\" >31.7</td>\n",
       "      <td id=\"T_e46a8_row9_col3\" class=\"data row9 col3\" >33.4</td>\n",
       "      <td id=\"T_e46a8_row9_col4\" class=\"data row9 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row9_col5\" class=\"data row9 col5\" >9.5</td>\n",
       "      <td id=\"T_e46a8_row9_col6\" class=\"data row9 col6\" >90.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row10_col0\" class=\"data row10 col0\" >The level of government spending relative to GDP in the U.S. should be reduced (disregarding expenditures for stabilization).</td>\n",
       "      <td id=\"T_e46a8_row10_col1\" class=\"data row10 col1\" >57.3</td>\n",
       "      <td id=\"T_e46a8_row10_col2\" class=\"data row10 col2\" >19.7</td>\n",
       "      <td id=\"T_e46a8_row10_col3\" class=\"data row10 col3\" >23.0</td>\n",
       "      <td id=\"T_e46a8_row10_col4\" class=\"data row10 col4\" >56.0</td>\n",
       "      <td id=\"T_e46a8_row10_col5\" class=\"data row10 col5\" >44.0</td>\n",
       "      <td id=\"T_e46a8_row10_col6\" class=\"data row10 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row11_col0\" class=\"data row11 col0\" >Macro models based on the assumption of a “representative, rational agent” yield generally useful and reasonably accurate predictions.</td>\n",
       "      <td id=\"T_e46a8_row11_col1\" class=\"data row11 col1\" >43.2</td>\n",
       "      <td id=\"T_e46a8_row11_col2\" class=\"data row11 col2\" >42.5</td>\n",
       "      <td id=\"T_e46a8_row11_col3\" class=\"data row11 col3\" >14.3</td>\n",
       "      <td id=\"T_e46a8_row11_col4\" class=\"data row11 col4\" >87.0</td>\n",
       "      <td id=\"T_e46a8_row11_col5\" class=\"data row11 col5\" >13.0</td>\n",
       "      <td id=\"T_e46a8_row11_col6\" class=\"data row11 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row12_col0\" class=\"data row12 col0\" >In the short run, a reduction in unemployment causes the rate of inflation to increase.</td>\n",
       "      <td id=\"T_e46a8_row12_col1\" class=\"data row12 col1\" >50.0</td>\n",
       "      <td id=\"T_e46a8_row12_col2\" class=\"data row12 col2\" >37.6</td>\n",
       "      <td id=\"T_e46a8_row12_col3\" class=\"data row12 col3\" >12.4</td>\n",
       "      <td id=\"T_e46a8_row12_col4\" class=\"data row12 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row12_col5\" class=\"data row12 col5\" >42.5</td>\n",
       "      <td id=\"T_e46a8_row12_col6\" class=\"data row12 col6\" >57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row13_col0\" class=\"data row13 col0\" >If the federal budget is to be balanced, it should be done over the course of the business cycle rather than yearly.</td>\n",
       "      <td id=\"T_e46a8_row13_col1\" class=\"data row13 col1\" >7.0</td>\n",
       "      <td id=\"T_e46a8_row13_col2\" class=\"data row13 col2\" >24.7</td>\n",
       "      <td id=\"T_e46a8_row13_col3\" class=\"data row13 col3\" >68.3</td>\n",
       "      <td id=\"T_e46a8_row13_col4\" class=\"data row13 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row13_col5\" class=\"data row13 col5\" >18.0</td>\n",
       "      <td id=\"T_e46a8_row13_col6\" class=\"data row13 col6\" >82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row14_col0\" class=\"data row14 col0\" >A large federal budget deficit has an adverse impact on the economy.</td>\n",
       "      <td id=\"T_e46a8_row14_col1\" class=\"data row14 col1\" >38.6</td>\n",
       "      <td id=\"T_e46a8_row14_col2\" class=\"data row14 col2\" >41.7</td>\n",
       "      <td id=\"T_e46a8_row14_col3\" class=\"data row14 col3\" >19.7</td>\n",
       "      <td id=\"T_e46a8_row14_col4\" class=\"data row14 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row14_col5\" class=\"data row14 col5\" >97.5</td>\n",
       "      <td id=\"T_e46a8_row14_col6\" class=\"data row14 col6\" >2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row15_col0\" class=\"data row15 col0\" >Fiscal policy (e.g. tax cut and/or expenditure increase) has a significant stimulative impact on a less than fully employed economy.</td>\n",
       "      <td id=\"T_e46a8_row15_col1\" class=\"data row15 col1\" >5.9</td>\n",
       "      <td id=\"T_e46a8_row15_col2\" class=\"data row15 col2\" >31.5</td>\n",
       "      <td id=\"T_e46a8_row15_col3\" class=\"data row15 col3\" >62.6</td>\n",
       "      <td id=\"T_e46a8_row15_col4\" class=\"data row15 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row15_col5\" class=\"data row15 col5\" >39.5</td>\n",
       "      <td id=\"T_e46a8_row15_col6\" class=\"data row15 col6\" >60.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row16_col0\" class=\"data row16 col0\" >Appropriately designed fiscal policy can increase the long-run rate of capital formation and economic growth.</td>\n",
       "      <td id=\"T_e46a8_row16_col1\" class=\"data row16 col1\" >9.6</td>\n",
       "      <td id=\"T_e46a8_row16_col2\" class=\"data row16 col2\" >27.0</td>\n",
       "      <td id=\"T_e46a8_row16_col3\" class=\"data row16 col3\" >63.4</td>\n",
       "      <td id=\"T_e46a8_row16_col4\" class=\"data row16 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row16_col5\" class=\"data row16 col5\" >90.0</td>\n",
       "      <td id=\"T_e46a8_row16_col6\" class=\"data row16 col6\" >10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row17_col0\" class=\"data row17 col0\" >Management of the business cycle should be left to the Federal Reserve; activist fiscal policies should be avoided.</td>\n",
       "      <td id=\"T_e46a8_row17_col1\" class=\"data row17 col1\" >66.6</td>\n",
       "      <td id=\"T_e46a8_row17_col2\" class=\"data row17 col2\" >21.2</td>\n",
       "      <td id=\"T_e46a8_row17_col3\" class=\"data row17 col3\" >12.2</td>\n",
       "      <td id=\"T_e46a8_row17_col4\" class=\"data row17 col4\" >82.5</td>\n",
       "      <td id=\"T_e46a8_row17_col5\" class=\"data row17 col5\" >17.5</td>\n",
       "      <td id=\"T_e46a8_row17_col6\" class=\"data row17 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row18_col0\" class=\"data row18 col0\" >Inflation is caused primarily by too much growth in the money supply.</td>\n",
       "      <td id=\"T_e46a8_row18_col1\" class=\"data row18 col1\" >29.2</td>\n",
       "      <td id=\"T_e46a8_row18_col2\" class=\"data row18 col2\" >36.9</td>\n",
       "      <td id=\"T_e46a8_row18_col3\" class=\"data row18 col3\" >33.9</td>\n",
       "      <td id=\"T_e46a8_row18_col4\" class=\"data row18 col4\" >4.0</td>\n",
       "      <td id=\"T_e46a8_row18_col5\" class=\"data row18 col5\" >96.0</td>\n",
       "      <td id=\"T_e46a8_row18_col6\" class=\"data row18 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row19_col0\" class=\"data row19 col0\" >The distribution of income in the U.S. should be more equal.</td>\n",
       "      <td id=\"T_e46a8_row19_col1\" class=\"data row19 col1\" >14.2</td>\n",
       "      <td id=\"T_e46a8_row19_col2\" class=\"data row19 col2\" >20.6</td>\n",
       "      <td id=\"T_e46a8_row19_col3\" class=\"data row19 col3\" >65.2</td>\n",
       "      <td id=\"T_e46a8_row19_col4\" class=\"data row19 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row19_col5\" class=\"data row19 col5\" >2.5</td>\n",
       "      <td id=\"T_e46a8_row19_col6\" class=\"data row19 col6\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row20_col0\" class=\"data row20 col0\" >The Federal Reserve should focus on a low rate of inflation rather than other goals such as employment, economic growth, or asset bubbles.</td>\n",
       "      <td id=\"T_e46a8_row20_col1\" class=\"data row20 col1\" >61.6</td>\n",
       "      <td id=\"T_e46a8_row20_col2\" class=\"data row20 col2\" >20.5</td>\n",
       "      <td id=\"T_e46a8_row20_col3\" class=\"data row20 col3\" >18.0</td>\n",
       "      <td id=\"T_e46a8_row20_col4\" class=\"data row20 col4\" >100.0</td>\n",
       "      <td id=\"T_e46a8_row20_col5\" class=\"data row20 col5\" >0</td>\n",
       "      <td id=\"T_e46a8_row20_col6\" class=\"data row20 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row21_col0\" class=\"data row21 col0\" >The Earned Income Tax Credit program should be expanded.</td>\n",
       "      <td id=\"T_e46a8_row21_col1\" class=\"data row21 col1\" >9.9</td>\n",
       "      <td id=\"T_e46a8_row21_col2\" class=\"data row21 col2\" >30.0</td>\n",
       "      <td id=\"T_e46a8_row21_col3\" class=\"data row21 col3\" >60.1</td>\n",
       "      <td id=\"T_e46a8_row21_col4\" class=\"data row21 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row21_col5\" class=\"data row21 col5\" >1.5</td>\n",
       "      <td id=\"T_e46a8_row21_col6\" class=\"data row21 col6\" >98.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row22_col0\" class=\"data row22 col0\" >During the pandemic, there is a trade-off between economic well- being and public health measures.trade-off between economic well-being and public health measures</td>\n",
       "      <td id=\"T_e46a8_row22_col1\" class=\"data row22 col1\" >43.7</td>\n",
       "      <td id=\"T_e46a8_row22_col2\" class=\"data row22 col2\" >22.4</td>\n",
       "      <td id=\"T_e46a8_row22_col3\" class=\"data row22 col3\" >33.9</td>\n",
       "      <td id=\"T_e46a8_row22_col4\" class=\"data row22 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row22_col5\" class=\"data row22 col5\" >78.5</td>\n",
       "      <td id=\"T_e46a8_row22_col6\" class=\"data row22 col6\" >21.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row23_col0\" class=\"data row23 col0\" >The distribution of income and wealth has little, if any, impact on economic stability and growth.</td>\n",
       "      <td id=\"T_e46a8_row23_col1\" class=\"data row23 col1\" >77.7</td>\n",
       "      <td id=\"T_e46a8_row23_col2\" class=\"data row23 col2\" >16.2</td>\n",
       "      <td id=\"T_e46a8_row23_col3\" class=\"data row23 col3\" >6.1</td>\n",
       "      <td id=\"T_e46a8_row23_col4\" class=\"data row23 col4\" >100.0</td>\n",
       "      <td id=\"T_e46a8_row23_col5\" class=\"data row23 col5\" >0</td>\n",
       "      <td id=\"T_e46a8_row23_col6\" class=\"data row23 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row24_col0\" class=\"data row24 col0\" >Immigration generally has a net positive economic effect for the US economy.</td>\n",
       "      <td id=\"T_e46a8_row24_col1\" class=\"data row24 col1\" >3.0</td>\n",
       "      <td id=\"T_e46a8_row24_col2\" class=\"data row24 col2\" >19.4</td>\n",
       "      <td id=\"T_e46a8_row24_col3\" class=\"data row24 col3\" >77.6</td>\n",
       "      <td id=\"T_e46a8_row24_col4\" class=\"data row24 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row24_col5\" class=\"data row24 col5\" >0.5</td>\n",
       "      <td id=\"T_e46a8_row24_col6\" class=\"data row24 col6\" >99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row25_col0\" class=\"data row25 col0\" >Redistribution of income is a legitimate role for the US Government.</td>\n",
       "      <td id=\"T_e46a8_row25_col1\" class=\"data row25 col1\" >13.7</td>\n",
       "      <td id=\"T_e46a8_row25_col2\" class=\"data row25 col2\" >22.3</td>\n",
       "      <td id=\"T_e46a8_row25_col3\" class=\"data row25 col3\" >64.0</td>\n",
       "      <td id=\"T_e46a8_row25_col4\" class=\"data row25 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row25_col5\" class=\"data row25 col5\" >95.5</td>\n",
       "      <td id=\"T_e46a8_row25_col6\" class=\"data row25 col6\" >4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row26_col0\" class=\"data row26 col0\" >Climate change poses a major risk to the US economy.</td>\n",
       "      <td id=\"T_e46a8_row26_col1\" class=\"data row26 col1\" >14.0</td>\n",
       "      <td id=\"T_e46a8_row26_col2\" class=\"data row26 col2\" >14.3</td>\n",
       "      <td id=\"T_e46a8_row26_col3\" class=\"data row26 col3\" >71.7</td>\n",
       "      <td id=\"T_e46a8_row26_col4\" class=\"data row26 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row26_col5\" class=\"data row26 col5\" >0</td>\n",
       "      <td id=\"T_e46a8_row26_col6\" class=\"data row26 col6\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row27_col0\" class=\"data row27 col0\" >A minimum wage increases unemployment among young and unskilled workers.</td>\n",
       "      <td id=\"T_e46a8_row27_col1\" class=\"data row27 col1\" >35.0</td>\n",
       "      <td id=\"T_e46a8_row27_col2\" class=\"data row27 col2\" >35.1</td>\n",
       "      <td id=\"T_e46a8_row27_col3\" class=\"data row27 col3\" >29.8</td>\n",
       "      <td id=\"T_e46a8_row27_col4\" class=\"data row27 col4\" >67.5</td>\n",
       "      <td id=\"T_e46a8_row27_col5\" class=\"data row27 col5\" >32.5</td>\n",
       "      <td id=\"T_e46a8_row27_col6\" class=\"data row27 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row28_col0\" class=\"data row28 col0\" >Welfare reforms which place time limits on public assistance have increased the general well- being of society.</td>\n",
       "      <td id=\"T_e46a8_row28_col1\" class=\"data row28 col1\" >45.9</td>\n",
       "      <td id=\"T_e46a8_row28_col2\" class=\"data row28 col2\" >32.7</td>\n",
       "      <td id=\"T_e46a8_row28_col3\" class=\"data row28 col3\" >21.4</td>\n",
       "      <td id=\"T_e46a8_row28_col4\" class=\"data row28 col4\" >95.0</td>\n",
       "      <td id=\"T_e46a8_row28_col5\" class=\"data row28 col5\" >5.0</td>\n",
       "      <td id=\"T_e46a8_row28_col6\" class=\"data row28 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row29_col0\" class=\"data row29 col0\" >The competitive model is generally more useful for understanding the U.S. economy than are game theoretic models of imperfect competition or collusion.</td>\n",
       "      <td id=\"T_e46a8_row29_col1\" class=\"data row29 col1\" >53.5</td>\n",
       "      <td id=\"T_e46a8_row29_col2\" class=\"data row29 col2\" >30.1</td>\n",
       "      <td id=\"T_e46a8_row29_col3\" class=\"data row29 col3\" >16.4</td>\n",
       "      <td id=\"T_e46a8_row29_col4\" class=\"data row29 col4\" >94.5</td>\n",
       "      <td id=\"T_e46a8_row29_col5\" class=\"data row29 col5\" >5.5</td>\n",
       "      <td id=\"T_e46a8_row29_col6\" class=\"data row29 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row30_col0\" class=\"data row30 col0\" >Pollution taxes or marketable pollution permits are a more efficient approach to pollution control than emission standards.</td>\n",
       "      <td id=\"T_e46a8_row30_col1\" class=\"data row30 col1\" >12.2</td>\n",
       "      <td id=\"T_e46a8_row30_col2\" class=\"data row30 col2\" >27.8</td>\n",
       "      <td id=\"T_e46a8_row30_col3\" class=\"data row30 col3\" >60.0</td>\n",
       "      <td id=\"T_e46a8_row30_col4\" class=\"data row30 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row30_col5\" class=\"data row30 col5\" >20.5</td>\n",
       "      <td id=\"T_e46a8_row30_col6\" class=\"data row30 col6\" >79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row31_col0\" class=\"data row31 col0\" >Easing restrictions on immigration will depress the average wage rate in the United States.</td>\n",
       "      <td id=\"T_e46a8_row31_col1\" class=\"data row31 col1\" >63.8</td>\n",
       "      <td id=\"T_e46a8_row31_col2\" class=\"data row31 col2\" >24.3</td>\n",
       "      <td id=\"T_e46a8_row31_col3\" class=\"data row31 col3\" >11.9</td>\n",
       "      <td id=\"T_e46a8_row31_col4\" class=\"data row31 col4\" >99.0</td>\n",
       "      <td id=\"T_e46a8_row31_col5\" class=\"data row31 col5\" >1.0</td>\n",
       "      <td id=\"T_e46a8_row31_col6\" class=\"data row31 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row32_col0\" class=\"data row32 col0\" >The long run benefits of higher taxes on fossil fuels outweigh the short run economic costs.</td>\n",
       "      <td id=\"T_e46a8_row32_col1\" class=\"data row32 col1\" >11.9</td>\n",
       "      <td id=\"T_e46a8_row32_col2\" class=\"data row32 col2\" >15.0</td>\n",
       "      <td id=\"T_e46a8_row32_col3\" class=\"data row32 col3\" >73.1</td>\n",
       "      <td id=\"T_e46a8_row32_col4\" class=\"data row32 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row32_col5\" class=\"data row32 col5\" >4.0</td>\n",
       "      <td id=\"T_e46a8_row32_col6\" class=\"data row32 col6\" >96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row33_col0\" class=\"data row33 col0\" >Antitrust laws should be enforced vigorously.</td>\n",
       "      <td id=\"T_e46a8_row33_col1\" class=\"data row33 col1\" >7.0</td>\n",
       "      <td id=\"T_e46a8_row33_col2\" class=\"data row33 col2\" >25.2</td>\n",
       "      <td id=\"T_e46a8_row33_col3\" class=\"data row33 col3\" >67.8</td>\n",
       "      <td id=\"T_e46a8_row33_col4\" class=\"data row33 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row33_col5\" class=\"data row33 col5\" >3.0</td>\n",
       "      <td id=\"T_e46a8_row33_col6\" class=\"data row33 col6\" >97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row34_col0\" class=\"data row34 col0\" >Reducing the tax rate on income from capital gains would encourage investment and promote economic growth.</td>\n",
       "      <td id=\"T_e46a8_row34_col1\" class=\"data row34 col1\" >53.5</td>\n",
       "      <td id=\"T_e46a8_row34_col2\" class=\"data row34 col2\" >25.9</td>\n",
       "      <td id=\"T_e46a8_row34_col3\" class=\"data row34 col3\" >20.6</td>\n",
       "      <td id=\"T_e46a8_row34_col4\" class=\"data row34 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row34_col5\" class=\"data row34 col5\" >99.0</td>\n",
       "      <td id=\"T_e46a8_row34_col6\" class=\"data row34 col6\" >1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row35_col0\" class=\"data row35 col0\" >There are few gender compensation and promotion differentials unexplained by differences in career and/or life choices.</td>\n",
       "      <td id=\"T_e46a8_row35_col1\" class=\"data row35 col1\" >58.6</td>\n",
       "      <td id=\"T_e46a8_row35_col2\" class=\"data row35 col2\" >20.6</td>\n",
       "      <td id=\"T_e46a8_row35_col3\" class=\"data row35 col3\" >20.8</td>\n",
       "      <td id=\"T_e46a8_row35_col4\" class=\"data row35 col4\" >43.0</td>\n",
       "      <td id=\"T_e46a8_row35_col5\" class=\"data row35 col5\" >52.5</td>\n",
       "      <td id=\"T_e46a8_row35_col6\" class=\"data row35 col6\" >4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row36_col0\" class=\"data row36 col0\" >Reducing the regulatory power of the Environmental Protection Agency (EPA) would improve the efficiency of the U.S. economy.</td>\n",
       "      <td id=\"T_e46a8_row36_col1\" class=\"data row36 col1\" >74.0</td>\n",
       "      <td id=\"T_e46a8_row36_col2\" class=\"data row36 col2\" >15.3</td>\n",
       "      <td id=\"T_e46a8_row36_col3\" class=\"data row36 col3\" >10.6</td>\n",
       "      <td id=\"T_e46a8_row36_col4\" class=\"data row36 col4\" >100.0</td>\n",
       "      <td id=\"T_e46a8_row36_col5\" class=\"data row36 col5\" >0</td>\n",
       "      <td id=\"T_e46a8_row36_col6\" class=\"data row36 col6\" >0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row37_col0\" class=\"data row37 col0\" >Lower marginal income tax rates increase the time spent at work and reduce time at leisure.</td>\n",
       "      <td id=\"T_e46a8_row37_col1\" class=\"data row37 col1\" >48.7</td>\n",
       "      <td id=\"T_e46a8_row37_col2\" class=\"data row37 col2\" >33.8</td>\n",
       "      <td id=\"T_e46a8_row37_col3\" class=\"data row37 col3\" >17.5</td>\n",
       "      <td id=\"T_e46a8_row37_col4\" class=\"data row37 col4\" >11.5</td>\n",
       "      <td id=\"T_e46a8_row37_col5\" class=\"data row37 col5\" >86.5</td>\n",
       "      <td id=\"T_e46a8_row37_col6\" class=\"data row37 col6\" >2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row38_col0\" class=\"data row38 col0\" >The structural U.S. federal deficit should be eliminated through a combination of lower expenditures and higher tax revenues.</td>\n",
       "      <td id=\"T_e46a8_row38_col1\" class=\"data row38 col1\" >36.5</td>\n",
       "      <td id=\"T_e46a8_row38_col2\" class=\"data row38 col2\" >39.4</td>\n",
       "      <td id=\"T_e46a8_row38_col3\" class=\"data row38 col3\" >24.2</td>\n",
       "      <td id=\"T_e46a8_row38_col4\" class=\"data row38 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row38_col5\" class=\"data row38 col5\" >81.0</td>\n",
       "      <td id=\"T_e46a8_row38_col6\" class=\"data row38 col6\" >19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row39_col0\" class=\"data row39 col0\" >The increasing inequality in the distribution of income in the U.S. is due primarily to the benefits and pressures of a global economy.</td>\n",
       "      <td id=\"T_e46a8_row39_col1\" class=\"data row39 col1\" >64.1</td>\n",
       "      <td id=\"T_e46a8_row39_col2\" class=\"data row39 col2\" >25.4</td>\n",
       "      <td id=\"T_e46a8_row39_col3\" class=\"data row39 col3\" >10.5</td>\n",
       "      <td id=\"T_e46a8_row39_col4\" class=\"data row39 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row39_col5\" class=\"data row39 col5\" >97.5</td>\n",
       "      <td id=\"T_e46a8_row39_col6\" class=\"data row39 col6\" >2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row40_col0\" class=\"data row40 col0\" >Addressing biases in individuals and institutions can improve both equity and efficiency.and institutions can improve both</td>\n",
       "      <td id=\"T_e46a8_row40_col1\" class=\"data row40 col1\" >10.0</td>\n",
       "      <td id=\"T_e46a8_row40_col2\" class=\"data row40 col2\" >25.3</td>\n",
       "      <td id=\"T_e46a8_row40_col3\" class=\"data row40 col3\" >64.8</td>\n",
       "      <td id=\"T_e46a8_row40_col4\" class=\"data row40 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row40_col5\" class=\"data row40 col5\" >2.5</td>\n",
       "      <td id=\"T_e46a8_row40_col6\" class=\"data row40 col6\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row41_col0\" class=\"data row41 col0\" >Differences in economic outcomes between whites and blacks in the US are in large part due to the persistence of discriminatory norms and institutions.</td>\n",
       "      <td id=\"T_e46a8_row41_col1\" class=\"data row41 col1\" >22.1</td>\n",
       "      <td id=\"T_e46a8_row41_col2\" class=\"data row41 col2\" >23.8</td>\n",
       "      <td id=\"T_e46a8_row41_col3\" class=\"data row41 col3\" >54.1</td>\n",
       "      <td id=\"T_e46a8_row41_col4\" class=\"data row41 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row41_col5\" class=\"data row41 col5\" >0.5</td>\n",
       "      <td id=\"T_e46a8_row41_col6\" class=\"data row41 col6\" >99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row42_col0\" class=\"data row42 col0\" >Corporate economic power has become too concentrated.</td>\n",
       "      <td id=\"T_e46a8_row42_col1\" class=\"data row42 col1\" >14.8</td>\n",
       "      <td id=\"T_e46a8_row42_col2\" class=\"data row42 col2\" >22.6</td>\n",
       "      <td id=\"T_e46a8_row42_col3\" class=\"data row42 col3\" >62.6</td>\n",
       "      <td id=\"T_e46a8_row42_col4\" class=\"data row42 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row42_col5\" class=\"data row42 col5\" >2.0</td>\n",
       "      <td id=\"T_e46a8_row42_col6\" class=\"data row42 col6\" >98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row43_col0\" class=\"data row43 col0\" >Lab experiments and randomized controlled trials are one of the most effective tools to identify causal effects and evaluate policies.</td>\n",
       "      <td id=\"T_e46a8_row43_col1\" class=\"data row43 col1\" >22.4</td>\n",
       "      <td id=\"T_e46a8_row43_col2\" class=\"data row43 col2\" >45.3</td>\n",
       "      <td id=\"T_e46a8_row43_col3\" class=\"data row43 col3\" >32.2</td>\n",
       "      <td id=\"T_e46a8_row43_col4\" class=\"data row43 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row43_col5\" class=\"data row43 col5\" >73.5</td>\n",
       "      <td id=\"T_e46a8_row43_col6\" class=\"data row43 col6\" >26.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row44_col0\" class=\"data row44 col0\" >Universal health insurance coverage will increase economic welfare in the United States.</td>\n",
       "      <td id=\"T_e46a8_row44_col1\" class=\"data row44 col1\" >12.2</td>\n",
       "      <td id=\"T_e46a8_row44_col2\" class=\"data row44 col2\" >19.2</td>\n",
       "      <td id=\"T_e46a8_row44_col3\" class=\"data row44 col3\" >68.6</td>\n",
       "      <td id=\"T_e46a8_row44_col4\" class=\"data row44 col4\" >0</td>\n",
       "      <td id=\"T_e46a8_row44_col5\" class=\"data row44 col5\" >31.0</td>\n",
       "      <td id=\"T_e46a8_row44_col6\" class=\"data row44 col6\" >69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e46a8_row45_col0\" class=\"data row45 col0\" >The US economy provides sufficient opportunities for social mobility.</td>\n",
       "      <td id=\"T_e46a8_row45_col1\" class=\"data row45 col1\" >52.3</td>\n",
       "      <td id=\"T_e46a8_row45_col2\" class=\"data row45 col2\" >30.0</td>\n",
       "      <td id=\"T_e46a8_row45_col3\" class=\"data row45 col3\" >17.7</td>\n",
       "      <td id=\"T_e46a8_row45_col4\" class=\"data row45 col4\" >6.0</td>\n",
       "      <td id=\"T_e46a8_row45_col5\" class=\"data row45 col5\" >94.0</td>\n",
       "      <td id=\"T_e46a8_row45_col6\" class=\"data row45 col6\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f3455653130>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alltables[['Claim', 'Disagree', 'Agree in Proviso', 'Agree', 'ChatGPT4o Disagree', 'ChatGPT4o Agree in Proviso', 'ChatGPT4o Agree']].style.set_caption(\"Table A3 - Geide-Stevenson and Alvaro La Parra Perez (forthcoming)\").set_table_styles(styles).format(precision=1).hide(axis='index')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b10b8c6-c16c-4c14-a84b-231922991f91",
   "metadata": {},
   "source": [
    "# Javdani and Chang (2023) statements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b3fb4f4e-6223-4a66-8cf4-002d90b195a2",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statement</th>\n",
       "      <th>mainstream</th>\n",
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       "      <th>Unnamed: 10</th>\n",
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       "      <th>0</th>\n",
       "      <td>“When we expect redistributive effects to even...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Paul Krugman, Professor of Economics at Prince...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Paul Krugman, Professor of Economics at Prince...</td>\n",
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       "      <td>0.14</td>\n",
       "      <td>0.54</td>\n",
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       "      <th>1</th>\n",
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       "      <td>David Levine, Professor of Economics at Washin...</td>\n",
       "      <td>Richard Wolff, Professor Emeritus of Economics...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.01</td>\n",
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       "      <td>“It is only in combination with particular, no...</td>\n",
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       "      <td>Sigmund Freud (1856-1939), the founder of psyc...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>“The very wealthy have little need for state-p...</td>\n",
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       "      <td>Thomas Piketty, Professor of Economics at the ...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.20</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>“Unlike most other science and social science ...</td>\n",
       "      <td>Carmen Reinhart, Professor of the Internationa...</td>\n",
       "      <td>Diane Elson, British Economist and Sociologist...</td>\n",
       "      <td>Carmen Reinhart, Professor of the Internationa...</td>\n",
       "      <td>Diane Elson, British Economist and Sociologist...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>“Economic discourse of any sort - verbal, math...</td>\n",
       "      <td>Ronald Coase (1910-2013), Professor of Economi...</td>\n",
       "      <td>William Milberg, Dean and Professor of Economi...</td>\n",
       "      <td>William Milberg, Dean and Professor of Economi...</td>\n",
       "      <td>Ronald Coase (1910-2013), Professor of Economi...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.00</td>\n",
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       "      <td>Irving Fisher (1867-1947), Professor of Politi...</td>\n",
       "      <td>John Kenneth Galbraith (1908-2006), Professor ...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.99</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>“The market economy has depended for its own w...</td>\n",
       "      <td>Amartya Sen, Professor of Economics and Philos...</td>\n",
       "      <td>Michael Sandel, American political philosopher...</td>\n",
       "      <td>Amartya Sen, Professor of Economics and Philos...</td>\n",
       "      <td>Michael Sandel, American political philosopher...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>1.01</td>\n",
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       "      <th>8</th>\n",
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       "      <td>Friedrich Engels (1820-1895), a German philoso...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>“Sharp increases in unemployment beyond the bu...</td>\n",
       "      <td>Larry Summers, Professor of Economics and Pres...</td>\n",
       "      <td>Yanis Varoufakis, Greek economist who also ser...</td>\n",
       "      <td>Larry Summers, Professor of Economics and Pres...</td>\n",
       "      <td>Yanis Varoufakis, Greek economist who also ser...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>“It is a great fault of symbolic pseudo-mathem...</td>\n",
       "      <td>Kenneth Arrow, Professor of Economics at Stanf...</td>\n",
       "      <td>John Maynard Keynes (1883-1946), Professor of ...</td>\n",
       "      <td>John Maynard Keynes (1883-1946), Professor of ...</td>\n",
       "      <td>Kenneth Arrow, Professor of Economics at Stanf...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>“From this failure to expunge the microeconomi...</td>\n",
       "      <td>Paul Romer, Professor of Economics at New York...</td>\n",
       "      <td>Steve Keen, post-Keynesian Professor of Econo...</td>\n",
       "      <td>Steve Keen, post-Keynesian Professor of Econo...</td>\n",
       "      <td>Paul Romer, Professor of Economics at New York...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>“In the progress of the division of labour, th...</td>\n",
       "      <td>Adam Smith.</td>\n",
       "      <td>Karl Marx.</td>\n",
       "      <td>Adam Smith.</td>\n",
       "      <td>Karl Marx.</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.00</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>“For four decades, since my time as a graduate...</td>\n",
       "      <td>Richard Thaler, Professor of Behavioural Scien...</td>\n",
       "      <td>Gerd Gigerenzer, Director at the Max Planck In...</td>\n",
       "      <td>Richard Thaler, Professor of Behavioural Scien...</td>\n",
       "      <td>Gerd Gigerenzer, Director at the Max Planck In...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>“There are powerful forces having to do with t...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Anwar Shaikh, Professor of Economics at the Ne...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Anwar Shaikh, Professor of Economics at the Ne...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.00</td>\n",
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      ],
      "text/plain": [
       "                                            statement  \\\n",
       "0   “When we expect redistributive effects to even...   \n",
       "1   “A realistic view of intellectual monopoly [e....   \n",
       "2   “It is only in combination with particular, no...   \n",
       "3   “The very wealthy have little need for state-p...   \n",
       "4   “Unlike most other science and social science ...   \n",
       "5   “Economic discourse of any sort - verbal, math...   \n",
       "6   “Academic economists, from their very open-min...   \n",
       "7   “The market economy has depended for its own w...   \n",
       "8   “The laws of property have made property of th...   \n",
       "9   “Sharp increases in unemployment beyond the bu...   \n",
       "10  “It is a great fault of symbolic pseudo-mathem...   \n",
       "11  “From this failure to expunge the microeconomi...   \n",
       "12  “In the progress of the division of labour, th...   \n",
       "13  “For four decades, since my time as a graduate...   \n",
       "14  “There are powerful forces having to do with t...   \n",
       "\n",
       "                                           mainstream  \\\n",
       "0   Dani Rodrik, Professor of International Politi...   \n",
       "1   David Levine, Professor of Economics at Washin...   \n",
       "2   Friedrich von Hayek (1899-1992), Professor of ...   \n",
       "3   Angus Deaton, Professor of Economics at Prince...   \n",
       "4   Carmen Reinhart, Professor of the Internationa...   \n",
       "5   Ronald Coase (1910-2013), Professor of Economi...   \n",
       "6   Irving Fisher (1867-1947), Professor of Politi...   \n",
       "7   Amartya Sen, Professor of Economics and Philos...   \n",
       "8   John Stuart Mill (1806-1873), an English philo...   \n",
       "9   Larry Summers, Professor of Economics and Pres...   \n",
       "10  Kenneth Arrow, Professor of Economics at Stanf...   \n",
       "11  Paul Romer, Professor of Economics at New York...   \n",
       "12                                        Adam Smith.   \n",
       "13  Richard Thaler, Professor of Behavioural Scien...   \n",
       "14  Dani Rodrik, Professor of International Politi...   \n",
       "\n",
       "                                          alternative  \\\n",
       "0   Paul Krugman, Professor of Economics at Prince...   \n",
       "1   Richard Wolff, Professor Emeritus of Economics...   \n",
       "2   Sigmund Freud (1856-1939), the founder of psyc...   \n",
       "3   Thomas Piketty, Professor of Economics at the ...   \n",
       "4   Diane Elson, British Economist and Sociologist...   \n",
       "5   William Milberg, Dean and Professor of Economi...   \n",
       "6   John Kenneth Galbraith (1908-2006), Professor ...   \n",
       "7   Michael Sandel, American political philosopher...   \n",
       "8   Friedrich Engels (1820-1895), a German philoso...   \n",
       "9   Yanis Varoufakis, Greek economist who also ser...   \n",
       "10  John Maynard Keynes (1883-1946), Professor of ...   \n",
       "11   Steve Keen, post-Keynesian Professor of Econo...   \n",
       "12                                         Karl Marx.   \n",
       "13  Gerd Gigerenzer, Director at the Max Planck In...   \n",
       "14  Anwar Shaikh, Professor of Economics at the Ne...   \n",
       "\n",
       "                                                 real  \\\n",
       "0   Dani Rodrik, Professor of International Politi...   \n",
       "1   David Levine, Professor of Economics at Washin...   \n",
       "2   Friedrich von Hayek (1899-1992), Professor of ...   \n",
       "3   Angus Deaton, Professor of Economics at Prince...   \n",
       "4   Carmen Reinhart, Professor of the Internationa...   \n",
       "5   William Milberg, Dean and Professor of Economi...   \n",
       "6   Irving Fisher (1867-1947), Professor of Politi...   \n",
       "7   Amartya Sen, Professor of Economics and Philos...   \n",
       "8   John Stuart Mill (1806-1873), an English philo...   \n",
       "9   Larry Summers, Professor of Economics and Pres...   \n",
       "10  John Maynard Keynes (1883-1946), Professor of ...   \n",
       "11   Steve Keen, post-Keynesian Professor of Econo...   \n",
       "12                                        Adam Smith.   \n",
       "13  Richard Thaler, Professor of Behavioural Scien...   \n",
       "14  Dani Rodrik, Professor of International Politi...   \n",
       "\n",
       "                                              altered  strongly disagree  \\\n",
       "0   Paul Krugman, Professor of Economics at Prince...               0.03   \n",
       "1   Richard Wolff, Professor Emeritus of Economics...               0.14   \n",
       "2   Sigmund Freud (1856-1939), the founder of psyc...               0.05   \n",
       "3   Thomas Piketty, Professor of Economics at the ...               0.08   \n",
       "4   Diane Elson, British Economist and Sociologist...               0.07   \n",
       "5   Ronald Coase (1910-2013), Professor of Economi...               0.09   \n",
       "6   John Kenneth Galbraith (1908-2006), Professor ...               0.03   \n",
       "7   Michael Sandel, American political philosopher...               0.01   \n",
       "8   Friedrich Engels (1820-1895), a German philoso...               0.09   \n",
       "9   Yanis Varoufakis, Greek economist who also ser...               0.06   \n",
       "10  Kenneth Arrow, Professor of Economics at Stanf...               0.08   \n",
       "11  Paul Romer, Professor of Economics at New York...               0.12   \n",
       "12                                         Karl Marx.               0.10   \n",
       "13  Gerd Gigerenzer, Director at the Max Planck In...               0.07   \n",
       "14  Anwar Shaikh, Professor of Economics at the Ne...               0.03   \n",
       "\n",
       "    disagree  neutral  agree  strongly agree  Unnamed: 10  \n",
       "0       0.14     0.14   0.54            0.13         0.98  \n",
       "1       0.45     0.18   0.19            0.05         1.01  \n",
       "2       0.20     0.32   0.34            0.09         1.00  \n",
       "3       0.23     0.10   0.39            0.20         1.00  \n",
       "4       0.19     0.16   0.36            0.21         0.99  \n",
       "5       0.22     0.17   0.35            0.17         1.00  \n",
       "6       0.20     0.22   0.45            0.09         0.99  \n",
       "7       0.04     0.13   0.48            0.35         1.01  \n",
       "8       0.25     0.23   0.31            0.12         1.00  \n",
       "9       0.13     0.12   0.40            0.29         1.00  \n",
       "10      0.19     0.16   0.35            0.22         1.00  \n",
       "11      0.27     0.24   0.29            0.08         1.00  \n",
       "12      0.31     0.25   0.27            0.07         1.00  \n",
       "13      0.20     0.17   0.40            0.16         1.00  \n",
       "14      0.12     0.13   0.45            0.27         1.00  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "realaltered=pd.read_excel('real altered.xlsx')\n",
    "realaltered\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "09d08a6a-0441-47d8-b2cf-5eb8d37df026",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# I ask ChatGPT what are the parameters used by the web version of chatgpt\n",
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "#claimtext= 'Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.'\n",
    "#claimtextinterpretation = 'When given the statement:' + claimtext + ', a person answers:' + claimanwswer + 'Do you think this answer means the person strongly disagrees with the statement, disagrees with the statement, is uncertain about the statement, agrees with the statement, or strongly agrees with the statement, or has no opinion about the statement. MAKE SURE your output is one of the categories stated only'\n",
    "\n",
    "n=200\n",
    "\n",
    "# they are all GPT 4o\n",
    "# 35 is without source attribution\n",
    "# 4o is with mainstream source attribution\n",
    "# 4oProf is with less/non-mainstream source attribution\n",
    "\n",
    "claimquestionanswergpt35=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "for j in range(0,15):\n",
    "    for i in range(0,n):\n",
    "        print(i)\n",
    "\n",
    "        #claimtextquestion= 'express your opinion on this statement:' + realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion=realaltered['statement'].loc[j] + ' Express your opinion on this statement by choosing among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "\n",
    "        \n",
    "        #claimtextquestion= 'Evaluate the following statement by ' + realaltered['mainstream'].loc[j] +' ' + realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion= 'Express your opinion on the following statement by ' + realaltered['mainstream'].loc[j] +' ' + realaltered['statement'].loc[j]  + ' For your answer choose among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "    \n",
    "        #claimtextquestion= 'Evaluate the following statement by ' + realaltered['alternative'].loc[j] +' '+ realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion= 'Express your opinion on the following statement by ' + realaltered['alternative'].loc[j] +' '+ realaltered['statement'].loc[j]  + ' For your answer choose among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        \n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "    \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "df['claimtext']=np.repeat(realaltered['statement'].to_list(),n)\n",
    "\n",
    "df.to_pickle('claimsrealaltered090824')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4953980d-b164-4fe7-9652-da93f5adac2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "claimquestionanswergpt4oclean\n",
      "agree             2184\n",
      "strongly agree     768\n",
      "neutral             48\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt4oprofclean\n",
      "agree             2193\n",
      "strongly agree     719\n",
      "neutral             87\n",
      "disagree             1\n",
      "Name: count, dtype: int64\n",
      "claimquestionanswergpt35clean\n",
      "agree             1952\n",
      "strongly agree     785\n",
      "neutral            263\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_pickle('claimsrealaltered090824')\n",
    "#df=pd.read_pickle('claimsrealaltered090824Prof')\n",
    "\n",
    "n=200\n",
    "\n",
    "# we start by cleaning the answers as ChatGPT does not always gives just the category as an answer (despite being asked to do so).\n",
    "# so we clean the answers by extracting the categorical answers from the raw answers\n",
    "\n",
    "df['claimquestionanswergpt4oclean']=None\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('neutral', case=False)]='neutral'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o'].str.contains('agree', case=False) & (df['claimquestionanswergpt4o'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4o'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oclean'].loc[df['claimquestionanswergpt4o']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt4oprofclean']=None\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('neutral', case=False)]='neutral'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof'].str.contains('agree', case=False) & (df['claimquestionanswergpt4oprof'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt4oprof'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "# one had agree as answer\n",
    "df['claimquestionanswergpt4oprofclean'].loc[df['claimquestionanswergpt4oprof']=='agre']='agree'\n",
    "print(df['claimquestionanswergpt4oprofclean'].value_counts())\n",
    "\n",
    "\n",
    "df['claimquestionanswergpt35clean']=None\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('neutral', case=False)]='neutral'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)]='strongly agree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)]='strongly disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('uncertain', case=False)]='uncertain'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('disagree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='disagree'\n",
    "df['claimquestionanswergpt35clean'].loc[df['claimquestionanswergpt35'].str.contains('agree', case=False) & (df['claimquestionanswergpt35'].str.contains('strongly agree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('disagree', case=False)==False)& (df['claimquestionanswergpt35'].str.contains('strongly disagree', case=False)==False)]='agree'\n",
    "print(df['claimquestionanswergpt35clean'].value_counts())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba1d3842-6c6b-42b8-9585-9f11b02a49b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a62ec caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_a62ec td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a62ec th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a62ec .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a62ec\">\n",
       "  <caption>Table - Does the Source Matter? - ChatGPT versus Economists</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a62ec_level0_col0\" class=\"col_heading level0 col0\" >Economists Survey</th>\n",
       "      <th id=\"T_a62ec_level0_col1\" class=\"col_heading level0 col1\" >ChatGPT Overall</th>\n",
       "      <th id=\"T_a62ec_level0_col2\" class=\"col_heading level0 col2\" >ChatGPT No Source</th>\n",
       "      <th id=\"T_a62ec_level0_col3\" class=\"col_heading level0 col3\" >ChatGPT Mainstream Source</th>\n",
       "      <th id=\"T_a62ec_level0_col4\" class=\"col_heading level0 col4\" >ChatGPT Less/Non-Mainstream Source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a62ec_level0_row0\" class=\"row_heading level0 row0\" >strongly agree</th>\n",
       "      <td id=\"T_a62ec_row0_col0\" class=\"data row0 col0\" >16.7</td>\n",
       "      <td id=\"T_a62ec_row0_col1\" class=\"data row0 col1\" >25.2</td>\n",
       "      <td id=\"T_a62ec_row0_col2\" class=\"data row0 col2\" >26.2</td>\n",
       "      <td id=\"T_a62ec_row0_col3\" class=\"data row0 col3\" >25.6</td>\n",
       "      <td id=\"T_a62ec_row0_col4\" class=\"data row0 col4\" >24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a62ec_level0_row1\" class=\"row_heading level0 row1\" >agree</th>\n",
       "      <td id=\"T_a62ec_row1_col0\" class=\"data row1 col0\" >37.1</td>\n",
       "      <td id=\"T_a62ec_row1_col1\" class=\"data row1 col1\" >70.3</td>\n",
       "      <td id=\"T_a62ec_row1_col2\" class=\"data row1 col2\" >65.1</td>\n",
       "      <td id=\"T_a62ec_row1_col3\" class=\"data row1 col3\" >72.8</td>\n",
       "      <td id=\"T_a62ec_row1_col4\" class=\"data row1 col4\" >73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a62ec_level0_row2\" class=\"row_heading level0 row2\" >neutral</th>\n",
       "      <td id=\"T_a62ec_row2_col0\" class=\"data row2 col0\" >18.1</td>\n",
       "      <td id=\"T_a62ec_row2_col1\" class=\"data row2 col1\" >4.4</td>\n",
       "      <td id=\"T_a62ec_row2_col2\" class=\"data row2 col2\" >8.8</td>\n",
       "      <td id=\"T_a62ec_row2_col3\" class=\"data row2 col3\" >1.6</td>\n",
       "      <td id=\"T_a62ec_row2_col4\" class=\"data row2 col4\" >2.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a62ec_level0_row3\" class=\"row_heading level0 row3\" >disagree</th>\n",
       "      <td id=\"T_a62ec_row3_col0\" class=\"data row3 col0\" >20.9</td>\n",
       "      <td id=\"T_a62ec_row3_col1\" class=\"data row3 col1\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row3_col2\" class=\"data row3 col2\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row3_col3\" class=\"data row3 col3\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row3_col4\" class=\"data row3 col4\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a62ec_level0_row4\" class=\"row_heading level0 row4\" >strongly disagree</th>\n",
       "      <td id=\"T_a62ec_row4_col0\" class=\"data row4 col0\" >7.0</td>\n",
       "      <td id=\"T_a62ec_row4_col1\" class=\"data row4 col1\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row4_col2\" class=\"data row4 col2\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row4_col3\" class=\"data row4 col3\" >0.0</td>\n",
       "      <td id=\"T_a62ec_row4_col4\" class=\"data row4 col4\" >0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd96cd5d8e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: This table is based on 15 survey questions from Javdani and Chang (2023).\n"
     ]
    }
   ],
   "source": [
    "a=df['claimquestionanswergpt35clean'].value_counts()/3000\n",
    "a=a.rename('ChatGPT No Source')\n",
    "b=df['claimquestionanswergpt4oclean'].value_counts()/3000\n",
    "b=b.rename('ChatGPT Mainstream Source')\n",
    "c=df['claimquestionanswergpt4oprofclean'].value_counts()/3000\n",
    "c=c.rename('ChatGPT Less/Non-Mainstream Source')\n",
    "d=pd.concat([a,b,c],axis=1).fillna(0).mean(axis=1)\n",
    "d=d.rename('ChatGPT Overall')\n",
    "\n",
    "e=realaltered[['strongly disagree', 'disagree', 'neutral', 'agree', 'strongly agree']].describe().loc['mean']\n",
    "e=e.rename('Economists Survey')\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'border-right:solid; border-color:blue; vertical-align:top'}]\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "q=pd.concat([e,d,a,b,c], axis=1).fillna(0)*100\n",
    "display(q.loc[['strongly agree', 'agree', 'neutral', 'disagree', 'strongly disagree']].style.format(precision=1).set_caption('Table - Does the Source Matter? - ChatGPT versus Economists').set_table_styles(styles))\n",
    "print('Notes: This table is based on 15 survey questions from Javdani and Chang (2023).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3ce96e3-1a73-4037-b5b8-376c94da7644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ChatGPT4o</th>\n",
       "      <th>Economists</th>\n",
       "      <th>ChatGPT4o no source</th>\n",
       "      <th>ChatGPT4o mainstream source</th>\n",
       "      <th>ChatGPT4o less/no mainstream source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.560000</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.285</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.991667</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.975</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.955000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.865</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.54</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.69</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.896667</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.935</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.938333</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.993333</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.980</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ChatGPT4o  Economists  ChatGPT4o no source  ChatGPT4o mainstream source  \\\n",
       "0    1.000000        0.67                1.000                        1.000   \n",
       "1    0.560000        0.24                0.285                        0.830   \n",
       "2    0.991667        0.43                0.975                        1.000   \n",
       "3    0.955000        0.59                0.865                        1.000   \n",
       "4    1.000000        0.57                1.000                        1.000   \n",
       "5    1.000000        0.52                1.000                        1.000   \n",
       "6    1.000000        0.54                1.000                        1.000   \n",
       "7    1.000000        0.83                1.000                        1.000   \n",
       "8    1.000000        0.43                1.000                        1.000   \n",
       "9    1.000000        0.69                1.000                        1.000   \n",
       "10   1.000000        0.57                1.000                        1.000   \n",
       "11   0.896667        0.37                0.755                        0.935   \n",
       "12   0.938333        0.34                0.825                        0.995   \n",
       "13   1.000000        0.56                1.000                        1.000   \n",
       "14   0.993333        0.72                0.980                        1.000   \n",
       "\n",
       "    ChatGPT4o less/no mainstream source  \n",
       "0                                 1.000  \n",
       "1                                 0.565  \n",
       "2                                 1.000  \n",
       "3                                 1.000  \n",
       "4                                 1.000  \n",
       "5                                 1.000  \n",
       "6                                 1.000  \n",
       "7                                 1.000  \n",
       "8                                 1.000  \n",
       "9                                 1.000  \n",
       "10                                1.000  \n",
       "11                                1.000  \n",
       "12                                0.995  \n",
       "13                                1.000  \n",
       "14                                1.000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# by question\n",
    "import numpy as np\n",
    "realaltered\n",
    "ct=0\n",
    "for i in list(range(0,3000,200)):\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT agree']=np.sum(pd.concat([df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()/200,df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()/200, df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()/200], axis=1), axis=1)['agree']/3\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT agree']=0\n",
    "\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree']=np.sum(pd.concat([df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()/200,df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()/200, df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()/200], axis=1), axis=1)['strongly agree']/3\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree']=np.sum(pd.concat([df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()/200,df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()/200, df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()/200], axis=1), axis=1)['strongly disagree']/3\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree']=0\n",
    "    try:    \n",
    "        realaltered.at[ct, 'ChatGPT disagree']=np.sum(pd.concat([df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()/200,df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()/200, df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()/200], axis=1), axis=1)[ 'disagree']/3\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT disagree']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT neutral']=np.sum(pd.concat([df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()/200,df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()/200, df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()/200], axis=1), axis=1)['neutral']/3\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT neutral']=0\n",
    "    ct=ct+1\n",
    "\n",
    "a=np.sum(realaltered[['ChatGPT agree','ChatGPT strongly agree']], axis=1).rename('ChatGPT4o')\n",
    "b=np.sum(realaltered[['agree','strongly agree']], axis=1).rename('Economists')\n",
    "pd.concat([a,b], axis=1)\n",
    "\n",
    "# by question\n",
    "\n",
    "ct=0\n",
    "for i in list(range(0,3000,200)):\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT agree no source']=df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()['agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT agree no source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree no source']=df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()['strongly agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree no source']=0\n",
    "    \n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree no source']=df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()['strongly disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree no source']=0\n",
    "    try:    \n",
    "        realaltered.at[ct, 'ChatGPT disagree no source']=df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()['disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT disagree no source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT neutral no source']=df['claimquestionanswergpt35clean'].loc[i:i+199].value_counts()['neutral']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT neutral no source']=0\n",
    "    ct=ct+1\n",
    "\n",
    "c=np.sum(realaltered[['ChatGPT agree no source','ChatGPT strongly agree no source']], axis=1).rename('ChatGPT4o no source')\n",
    "\n",
    "\n",
    "\n",
    "# by question\n",
    "\n",
    "ct=0\n",
    "for i in list(range(0,3000,200)):\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT agree mainstream source']=df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()['agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT agree mainstream source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree mainstream source']=df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()['strongly agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree mainstream source']=0\n",
    "    \n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree mainstream source']=df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()['strongly disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree mainstream source']=0\n",
    "    try:    \n",
    "        realaltered.at[ct, 'ChatGPT disagree mainstream source']=df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()['disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT disagree mainstream source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT neutral mainstream source']=df['claimquestionanswergpt4oclean'].loc[i:i+199].value_counts()['neutral']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT neutral mainstream source']=0\n",
    "    ct=ct+1\n",
    "\n",
    "d=np.sum(realaltered[['ChatGPT agree mainstream source','ChatGPT strongly agree mainstream source']], axis=1).rename('ChatGPT4o mainstream source')\n",
    "\n",
    "ct=0\n",
    "for i in list(range(0,3000,200)):\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT agree less/no mainstream source']=df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()['agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT agree less/no mainstream source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree less/no mainstream source']=df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()['strongly agree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly agree less/no mainstream source']=0\n",
    "    \n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree less/no mainstream source']=df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()['strongly disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT strongly disagree less/no mainstream source']=0\n",
    "    try:    \n",
    "        realaltered.at[ct, 'ChatGPT disagree less/no mainstream source']=df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()['disagree']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT disagree less/no mainstream source']=0\n",
    "    try:\n",
    "        realaltered.at[ct, 'ChatGPT neutral less/no mainstream source']=df['claimquestionanswergpt4oprofclean'].loc[i:i+199].value_counts()['neutral']/200\n",
    "    except:\n",
    "        realaltered.at[ct, 'ChatGPT neutral less/no mainstream source']=0\n",
    "    ct=ct+1\n",
    "\n",
    "e=np.sum(realaltered[['ChatGPT agree less/no mainstream source','ChatGPT strongly agree less/no mainstream source']], axis=1).rename('ChatGPT4o less/no mainstream source')\n",
    "\n",
    "\n",
    "pd.concat([a,b,c,d,e], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca289542-3bcb-4523-a6ec-b2a86fc3dd00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statement</th>\n",
       "      <th>mainstream</th>\n",
       "      <th>alternative</th>\n",
       "      <th>real</th>\n",
       "      <th>altered</th>\n",
       "      <th>strongly disagree</th>\n",
       "      <th>disagree</th>\n",
       "      <th>neutral</th>\n",
       "      <th>agree</th>\n",
       "      <th>strongly agree</th>\n",
       "      <th>...</th>\n",
       "      <th>ChatGPT agree mainstream source</th>\n",
       "      <th>ChatGPT strongly agree mainstream source</th>\n",
       "      <th>ChatGPT strongly disagree mainstream source</th>\n",
       "      <th>ChatGPT disagree mainstream source</th>\n",
       "      <th>ChatGPT neutral mainstream source</th>\n",
       "      <th>ChatGPT agree less/no mainstream source</th>\n",
       "      <th>ChatGPT strongly agree less/no mainstream source</th>\n",
       "      <th>ChatGPT strongly disagree less/no mainstream source</th>\n",
       "      <th>ChatGPT disagree less/no mainstream source</th>\n",
       "      <th>ChatGPT neutral less/no mainstream source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>“When we expect redistributive effects to even...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Paul Krugman, Professor of Economics at Prince...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Paul Krugman, Professor of Economics at Prince...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.54</td>\n",
       "      <td>0.13</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>“A realistic view of intellectual monopoly [e....</td>\n",
       "      <td>David Levine, Professor of Economics at Washin...</td>\n",
       "      <td>Richard Wolff, Professor Emeritus of Economics...</td>\n",
       "      <td>David Levine, Professor of Economics at Washin...</td>\n",
       "      <td>Richard Wolff, Professor Emeritus of Economics...</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.05</td>\n",
       "      <td>...</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.735</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.025</td>\n",
       "      <td>0.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>“It is only in combination with particular, no...</td>\n",
       "      <td>Friedrich von Hayek (1899-1992), Professor of ...</td>\n",
       "      <td>Sigmund Freud (1856-1939), the founder of psyc...</td>\n",
       "      <td>Friedrich von Hayek (1899-1992), Professor of ...</td>\n",
       "      <td>Sigmund Freud (1856-1939), the founder of psyc...</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>“The very wealthy have little need for state-p...</td>\n",
       "      <td>Angus Deaton, Professor of Economics at Prince...</td>\n",
       "      <td>Thomas Piketty, Professor of Economics at the ...</td>\n",
       "      <td>Angus Deaton, Professor of Economics at Prince...</td>\n",
       "      <td>Thomas Piketty, Professor of Economics at the ...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.39</td>\n",
       "      <td>0.20</td>\n",
       "      <td>...</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.915</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>“Unlike most other science and social science ...</td>\n",
       "      <td>Carmen Reinhart, Professor of the Internationa...</td>\n",
       "      <td>Diane Elson, British Economist and Sociologist...</td>\n",
       "      <td>Carmen Reinhart, Professor of the Internationa...</td>\n",
       "      <td>Diane Elson, British Economist and Sociologist...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.21</td>\n",
       "      <td>...</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>“Economic discourse of any sort - verbal, math...</td>\n",
       "      <td>Ronald Coase (1910-2013), Professor of Economi...</td>\n",
       "      <td>William Milberg, Dean and Professor of Economi...</td>\n",
       "      <td>William Milberg, Dean and Professor of Economi...</td>\n",
       "      <td>Ronald Coase (1910-2013), Professor of Economi...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.17</td>\n",
       "      <td>...</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>“Academic economists, from their very open-min...</td>\n",
       "      <td>Irving Fisher (1867-1947), Professor of Politi...</td>\n",
       "      <td>John Kenneth Galbraith (1908-2006), Professor ...</td>\n",
       "      <td>Irving Fisher (1867-1947), Professor of Politi...</td>\n",
       "      <td>John Kenneth Galbraith (1908-2006), Professor ...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.990</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>“The market economy has depended for its own w...</td>\n",
       "      <td>Amartya Sen, Professor of Economics and Philos...</td>\n",
       "      <td>Michael Sandel, American political philosopher...</td>\n",
       "      <td>Amartya Sen, Professor of Economics and Philos...</td>\n",
       "      <td>Michael Sandel, American political philosopher...</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>“The laws of property have made property of th...</td>\n",
       "      <td>John Stuart Mill (1806-1873), an English philo...</td>\n",
       "      <td>Friedrich Engels (1820-1895), a German philoso...</td>\n",
       "      <td>John Stuart Mill (1806-1873), an English philo...</td>\n",
       "      <td>Friedrich Engels (1820-1895), a German philoso...</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.12</td>\n",
       "      <td>...</td>\n",
       "      <td>0.955</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.550</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>“Sharp increases in unemployment beyond the bu...</td>\n",
       "      <td>Larry Summers, Professor of Economics and Pres...</td>\n",
       "      <td>Yanis Varoufakis, Greek economist who also ser...</td>\n",
       "      <td>Larry Summers, Professor of Economics and Pres...</td>\n",
       "      <td>Yanis Varoufakis, Greek economist who also ser...</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.29</td>\n",
       "      <td>...</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>“It is a great fault of symbolic pseudo-mathem...</td>\n",
       "      <td>Kenneth Arrow, Professor of Economics at Stanf...</td>\n",
       "      <td>John Maynard Keynes (1883-1946), Professor of ...</td>\n",
       "      <td>John Maynard Keynes (1883-1946), Professor of ...</td>\n",
       "      <td>Kenneth Arrow, Professor of Economics at Stanf...</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.22</td>\n",
       "      <td>...</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.960</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>“From this failure to expunge the microeconomi...</td>\n",
       "      <td>Paul Romer, Professor of Economics at New York...</td>\n",
       "      <td>Steve Keen, post-Keynesian Professor of Econo...</td>\n",
       "      <td>Steve Keen, post-Keynesian Professor of Econo...</td>\n",
       "      <td>Paul Romer, Professor of Economics at New York...</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.08</td>\n",
       "      <td>...</td>\n",
       "      <td>0.450</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.520</td>\n",
       "      <td>0.980</td>\n",
       "      <td>0.020</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>“In the progress of the division of labour, th...</td>\n",
       "      <td>Adam Smith.</td>\n",
       "      <td>Karl Marx.</td>\n",
       "      <td>Adam Smith.</td>\n",
       "      <td>Karl Marx.</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.07</td>\n",
       "      <td>...</td>\n",
       "      <td>0.940</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.660</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.010</td>\n",
       "      <td>0.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>“For four decades, since my time as a graduate...</td>\n",
       "      <td>Richard Thaler, Professor of Behavioural Scien...</td>\n",
       "      <td>Gerd Gigerenzer, Director at the Max Planck In...</td>\n",
       "      <td>Richard Thaler, Professor of Behavioural Scien...</td>\n",
       "      <td>Gerd Gigerenzer, Director at the Max Planck In...</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.17</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.16</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.400</td>\n",
       "      <td>0.600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>“There are powerful forces having to do with t...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Anwar Shaikh, Professor of Economics at the Ne...</td>\n",
       "      <td>Dani Rodrik, Professor of International Politi...</td>\n",
       "      <td>Anwar Shaikh, Professor of Economics at the Ne...</td>\n",
       "      <td>0.03</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.27</td>\n",
       "      <td>...</td>\n",
       "      <td>0.985</td>\n",
       "      <td>0.015</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>15 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            statement  \\\n",
       "0   “When we expect redistributive effects to even...   \n",
       "1   “A realistic view of intellectual monopoly [e....   \n",
       "2   “It is only in combination with particular, no...   \n",
       "3   “The very wealthy have little need for state-p...   \n",
       "4   “Unlike most other science and social science ...   \n",
       "5   “Economic discourse of any sort - verbal, math...   \n",
       "6   “Academic economists, from their very open-min...   \n",
       "7   “The market economy has depended for its own w...   \n",
       "8   “The laws of property have made property of th...   \n",
       "9   “Sharp increases in unemployment beyond the bu...   \n",
       "10  “It is a great fault of symbolic pseudo-mathem...   \n",
       "11  “From this failure to expunge the microeconomi...   \n",
       "12  “In the progress of the division of labour, th...   \n",
       "13  “For four decades, since my time as a graduate...   \n",
       "14  “There are powerful forces having to do with t...   \n",
       "\n",
       "                                           mainstream  \\\n",
       "0   Dani Rodrik, Professor of International Politi...   \n",
       "1   David Levine, Professor of Economics at Washin...   \n",
       "2   Friedrich von Hayek (1899-1992), Professor of ...   \n",
       "3   Angus Deaton, Professor of Economics at Prince...   \n",
       "4   Carmen Reinhart, Professor of the Internationa...   \n",
       "5   Ronald Coase (1910-2013), Professor of Economi...   \n",
       "6   Irving Fisher (1867-1947), Professor of Politi...   \n",
       "7   Amartya Sen, Professor of Economics and Philos...   \n",
       "8   John Stuart Mill (1806-1873), an English philo...   \n",
       "9   Larry Summers, Professor of Economics and Pres...   \n",
       "10  Kenneth Arrow, Professor of Economics at Stanf...   \n",
       "11  Paul Romer, Professor of Economics at New York...   \n",
       "12                                        Adam Smith.   \n",
       "13  Richard Thaler, Professor of Behavioural Scien...   \n",
       "14  Dani Rodrik, Professor of International Politi...   \n",
       "\n",
       "                                          alternative  \\\n",
       "0   Paul Krugman, Professor of Economics at Prince...   \n",
       "1   Richard Wolff, Professor Emeritus of Economics...   \n",
       "2   Sigmund Freud (1856-1939), the founder of psyc...   \n",
       "3   Thomas Piketty, Professor of Economics at the ...   \n",
       "4   Diane Elson, British Economist and Sociologist...   \n",
       "5   William Milberg, Dean and Professor of Economi...   \n",
       "6   John Kenneth Galbraith (1908-2006), Professor ...   \n",
       "7   Michael Sandel, American political philosopher...   \n",
       "8   Friedrich Engels (1820-1895), a German philoso...   \n",
       "9   Yanis Varoufakis, Greek economist who also ser...   \n",
       "10  John Maynard Keynes (1883-1946), Professor of ...   \n",
       "11   Steve Keen, post-Keynesian Professor of Econo...   \n",
       "12                                         Karl Marx.   \n",
       "13  Gerd Gigerenzer, Director at the Max Planck In...   \n",
       "14  Anwar Shaikh, Professor of Economics at the Ne...   \n",
       "\n",
       "                                                 real  \\\n",
       "0   Dani Rodrik, Professor of International Politi...   \n",
       "1   David Levine, Professor of Economics at Washin...   \n",
       "2   Friedrich von Hayek (1899-1992), Professor of ...   \n",
       "3   Angus Deaton, Professor of Economics at Prince...   \n",
       "4   Carmen Reinhart, Professor of the Internationa...   \n",
       "5   William Milberg, Dean and Professor of Economi...   \n",
       "6   Irving Fisher (1867-1947), Professor of Politi...   \n",
       "7   Amartya Sen, Professor of Economics and Philos...   \n",
       "8   John Stuart Mill (1806-1873), an English philo...   \n",
       "9   Larry Summers, Professor of Economics and Pres...   \n",
       "10  John Maynard Keynes (1883-1946), Professor of ...   \n",
       "11   Steve Keen, post-Keynesian Professor of Econo...   \n",
       "12                                        Adam Smith.   \n",
       "13  Richard Thaler, Professor of Behavioural Scien...   \n",
       "14  Dani Rodrik, Professor of International Politi...   \n",
       "\n",
       "                                              altered  strongly disagree  \\\n",
       "0   Paul Krugman, Professor of Economics at Prince...               0.03   \n",
       "1   Richard Wolff, Professor Emeritus of Economics...               0.14   \n",
       "2   Sigmund Freud (1856-1939), the founder of psyc...               0.05   \n",
       "3   Thomas Piketty, Professor of Economics at the ...               0.08   \n",
       "4   Diane Elson, British Economist and Sociologist...               0.07   \n",
       "5   Ronald Coase (1910-2013), Professor of Economi...               0.09   \n",
       "6   John Kenneth Galbraith (1908-2006), Professor ...               0.03   \n",
       "7   Michael Sandel, American political philosopher...               0.01   \n",
       "8   Friedrich Engels (1820-1895), a German philoso...               0.09   \n",
       "9   Yanis Varoufakis, Greek economist who also ser...               0.06   \n",
       "10  Kenneth Arrow, Professor of Economics at Stanf...               0.08   \n",
       "11  Paul Romer, Professor of Economics at New York...               0.12   \n",
       "12                                         Karl Marx.               0.10   \n",
       "13  Gerd Gigerenzer, Director at the Max Planck In...               0.07   \n",
       "14  Anwar Shaikh, Professor of Economics at the Ne...               0.03   \n",
       "\n",
       "    disagree  neutral  agree  strongly agree  ...  \\\n",
       "0       0.14     0.14   0.54            0.13  ...   \n",
       "1       0.45     0.18   0.19            0.05  ...   \n",
       "2       0.20     0.32   0.34            0.09  ...   \n",
       "3       0.23     0.10   0.39            0.20  ...   \n",
       "4       0.19     0.16   0.36            0.21  ...   \n",
       "5       0.22     0.17   0.35            0.17  ...   \n",
       "6       0.20     0.22   0.45            0.09  ...   \n",
       "7       0.04     0.13   0.48            0.35  ...   \n",
       "8       0.25     0.23   0.31            0.12  ...   \n",
       "9       0.13     0.12   0.40            0.29  ...   \n",
       "10      0.19     0.16   0.35            0.22  ...   \n",
       "11      0.27     0.24   0.29            0.08  ...   \n",
       "12      0.31     0.25   0.27            0.07  ...   \n",
       "13      0.20     0.17   0.40            0.16  ...   \n",
       "14      0.12     0.13   0.45            0.27  ...   \n",
       "\n",
       "    ChatGPT agree mainstream source  ChatGPT strongly agree mainstream source  \\\n",
       "0                             0.780                                     0.220   \n",
       "1                             0.255                                     0.005   \n",
       "2                             0.985                                     0.015   \n",
       "3                             0.950                                     0.050   \n",
       "4                             0.180                                     0.820   \n",
       "5                             0.990                                     0.010   \n",
       "6                             0.990                                     0.010   \n",
       "7                             0.070                                     0.930   \n",
       "8                             0.955                                     0.045   \n",
       "9                             0.910                                     0.090   \n",
       "10                            0.980                                     0.020   \n",
       "11                            0.450                                     0.000   \n",
       "12                            0.940                                     0.000   \n",
       "13                            0.005                                     0.995   \n",
       "14                            0.985                                     0.015   \n",
       "\n",
       "    ChatGPT strongly disagree mainstream source  \\\n",
       "0                                           0.0   \n",
       "1                                           0.0   \n",
       "2                                           0.0   \n",
       "3                                           0.0   \n",
       "4                                           0.0   \n",
       "5                                           0.0   \n",
       "6                                           0.0   \n",
       "7                                           0.0   \n",
       "8                                           0.0   \n",
       "9                                           0.0   \n",
       "10                                          0.0   \n",
       "11                                          0.0   \n",
       "12                                          0.0   \n",
       "13                                          0.0   \n",
       "14                                          0.0   \n",
       "\n",
       "    ChatGPT disagree mainstream source  ChatGPT neutral mainstream source  \\\n",
       "0                                0.000                              0.000   \n",
       "1                                0.005                              0.735   \n",
       "2                                0.000                              0.000   \n",
       "3                                0.000                              0.000   \n",
       "4                                0.000                              0.000   \n",
       "5                                0.000                              0.000   \n",
       "6                                0.000                              0.000   \n",
       "7                                0.000                              0.000   \n",
       "8                                0.000                              0.000   \n",
       "9                                0.000                              0.000   \n",
       "10                               0.000                              0.000   \n",
       "11                               0.030                              0.520   \n",
       "12                               0.000                              0.060   \n",
       "13                               0.000                              0.000   \n",
       "14                               0.000                              0.000   \n",
       "\n",
       "    ChatGPT agree less/no mainstream source  \\\n",
       "0                                     0.890   \n",
       "1                                     0.000   \n",
       "2                                     0.985   \n",
       "3                                     0.915   \n",
       "4                                     0.110   \n",
       "5                                     0.955   \n",
       "6                                     0.990   \n",
       "7                                     0.200   \n",
       "8                                     0.550   \n",
       "9                                     0.950   \n",
       "10                                    0.960   \n",
       "11                                    0.980   \n",
       "12                                    0.660   \n",
       "13                                    0.400   \n",
       "14                                    0.970   \n",
       "\n",
       "    ChatGPT strongly agree less/no mainstream source  \\\n",
       "0                                              0.110   \n",
       "1                                              0.000   \n",
       "2                                              0.005   \n",
       "3                                              0.085   \n",
       "4                                              0.890   \n",
       "5                                              0.045   \n",
       "6                                              0.010   \n",
       "7                                              0.800   \n",
       "8                                              0.000   \n",
       "9                                              0.050   \n",
       "10                                             0.000   \n",
       "11                                             0.020   \n",
       "12                                             0.005   \n",
       "13                                             0.600   \n",
       "14                                             0.030   \n",
       "\n",
       "    ChatGPT strongly disagree less/no mainstream source  \\\n",
       "0                                                 0.0     \n",
       "1                                                 0.0     \n",
       "2                                                 0.0     \n",
       "3                                                 0.0     \n",
       "4                                                 0.0     \n",
       "5                                                 0.0     \n",
       "6                                                 0.0     \n",
       "7                                                 0.0     \n",
       "8                                                 0.0     \n",
       "9                                                 0.0     \n",
       "10                                                0.0     \n",
       "11                                                0.0     \n",
       "12                                                0.0     \n",
       "13                                                0.0     \n",
       "14                                                0.0     \n",
       "\n",
       "    ChatGPT disagree less/no mainstream source  \\\n",
       "0                                        0.000   \n",
       "1                                        0.025   \n",
       "2                                        0.000   \n",
       "3                                        0.000   \n",
       "4                                        0.000   \n",
       "5                                        0.000   \n",
       "6                                        0.000   \n",
       "7                                        0.000   \n",
       "8                                        0.000   \n",
       "9                                        0.000   \n",
       "10                                       0.000   \n",
       "11                                       0.000   \n",
       "12                                       0.010   \n",
       "13                                       0.000   \n",
       "14                                       0.000   \n",
       "\n",
       "    ChatGPT neutral less/no mainstream source  \n",
       "0                                       0.000  \n",
       "1                                       0.975  \n",
       "2                                       0.010  \n",
       "3                                       0.000  \n",
       "4                                       0.000  \n",
       "5                                       0.000  \n",
       "6                                       0.000  \n",
       "7                                       0.000  \n",
       "8                                       0.450  \n",
       "9                                       0.000  \n",
       "10                                      0.040  \n",
       "11                                      0.000  \n",
       "12                                      0.325  \n",
       "13                                      0.000  \n",
       "14                                      0.000  \n",
       "\n",
       "[15 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "realaltered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "82e7d6cd-b038-464c-8ad3-d32256b055e2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check to what extent most frequent answer is the same\n",
    "for i in range(0,15):\n",
    "    realaltered.at[i,'ChatGPT strongly agree']=np.sum(np.sum(df[df['claimtext']==realaltered['statement'].loc[i]][['claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean']]=='strongly agree'))/600\n",
    "    realaltered.at[i,'ChatGPT agree']=np.sum(np.sum(df[df['claimtext']==realaltered['statement'].loc[i]][['claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean']]=='agree'))/600\n",
    "    realaltered.at[i,'ChatGPT neutral']=np.sum(np.sum(df[df['claimtext']==realaltered['statement'].loc[i]][['claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean']]=='neutral'))/600\n",
    "    realaltered.at[i,'ChatGPT disagree']=np.sum(np.sum(df[df['claimtext']==realaltered['statement'].loc[i]][['claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean']]=='disagree'))/600\n",
    "    realaltered.at[i,'ChatGPT strongly disagree']=np.sum(np.sum(df[df['claimtext']==realaltered['statement'].loc[i]][['claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean','claimquestionanswergpt4oprofclean']]=='strongly disagree'))/600\n",
    "\n",
    "realaltered['Median']=realaltered[['strongly disagree', 'disagree', 'neutral', 'agree', 'strongly agree']].idxmax(axis=1)\n",
    "realaltered['ChatGPT Median']=realaltered[['ChatGPT strongly disagree', 'ChatGPT disagree', 'ChatGPT neutral', 'ChatGPT agree', 'ChatGPT strongly agree']].idxmax(axis=1).str.replace('ChatGPT ',\"\")\n",
    "np.sum(realaltered['Median']==realaltered['ChatGPT Median'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95823df7-8953-48b3-9420-ed9689a594e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.78</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 15 Aug 2024</td> <th>  Prob (F-statistic):</th> <td>2.86e-06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:19:45</td>     <th>  Log-Likelihood:    </th> <td> -7745.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  9000</td>      <th>  AIC:               </th> <td>1.550e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8997</td>      <th>  BIC:               </th> <td>1.552e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>    4.0663</td> <td>    0.010</td> <td>  389.185</td> <td> 0.000</td> <td>    4.046</td> <td>    4.087</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>     <td>    0.0563</td> <td>    0.015</td> <td>    3.812</td> <td> 0.000</td> <td>    0.027</td> <td>    0.085</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>     <td>   -0.0143</td> <td>    0.015</td> <td>   -0.970</td> <td> 0.332</td> <td>   -0.043</td> <td>    0.015</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>44.697</td> <th>  Durbin-Watson:     </th> <td>   0.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  56.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.082</td> <th>  Prob(JB):          </th> <td>4.33e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.354</td> <th>  Cond. No.          </th> <td>    3.73</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.003   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.003   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     12.78   \\\\\n",
       "\\textbf{Date:}             & Thu, 15 Aug 2024 & \\textbf{  Prob (F-statistic):} &  2.86e-06   \\\\\n",
       "\\textbf{Time:}             &     10:19:45     & \\textbf{  Log-Likelihood:    } &   -7745.8   \\\\\n",
       "\\textbf{No. Observations:} &        9000      & \\textbf{  AIC:               } & 1.550e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        8997      & \\textbf{  BIC:               } & 1.552e+04   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &       4.0663  &        0.010     &   389.185  &         0.000        &        4.046    &        4.087     \\\\\n",
       "\\textbf{2}     &       0.0563  &        0.015     &     3.812  &         0.000        &        0.027    &        0.085     \\\\\n",
       "\\textbf{3}     &      -0.0143  &        0.015     &    -0.970  &         0.332        &       -0.043    &        0.015     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 44.697 & \\textbf{  Durbin-Watson:     } &    0.542  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   56.937  \\\\\n",
       "\\textbf{Skew:}          & -0.082 & \\textbf{  Prob(JB):          } & 4.33e-13  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.354 & \\textbf{  Cond. No.          } &     3.73  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.003\n",
       "Model:                            OLS   Adj. R-squared:                  0.003\n",
       "Method:                 Least Squares   F-statistic:                     12.78\n",
       "Date:                Thu, 15 Aug 2024   Prob (F-statistic):           2.86e-06\n",
       "Time:                        10:19:45   Log-Likelihood:                -7745.8\n",
       "No. Observations:                9000   AIC:                         1.550e+04\n",
       "Df Residuals:                    8997   BIC:                         1.552e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          4.0663      0.010    389.185      0.000       4.046       4.087\n",
       "2              0.0563      0.015      3.812      0.000       0.027       0.085\n",
       "3             -0.0143      0.015     -0.970      0.332      -0.043       0.015\n",
       "==============================================================================\n",
       "Omnibus:                       44.697   Durbin-Watson:                   0.542\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.937\n",
       "Skew:                          -0.082   Prob(JB):                     4.33e-13\n",
       "Kurtosis:                       3.354   Cond. No.                         3.73\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.003</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   12.78</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 15 Aug 2024</td> <th>  Prob (F-statistic):</th> <td>2.86e-06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>10:19:45</td>     <th>  Log-Likelihood:    </th> <td> -12757.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  9000</td>      <th>  AIC:               </th> <td>2.552e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  8997</td>      <th>  BIC:               </th> <td>2.554e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th> <td>   -0.0244</td> <td>    0.018</td> <td>   -1.340</td> <td> 0.180</td> <td>   -0.060</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2</th>     <td>    0.0983</td> <td>    0.026</td> <td>    3.812</td> <td> 0.000</td> <td>    0.048</td> <td>    0.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>3</th>     <td>   -0.0250</td> <td>    0.026</td> <td>   -0.970</td> <td> 0.332</td> <td>   -0.076</td> <td>    0.026</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>44.697</td> <th>  Durbin-Watson:     </th> <td>   0.542</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  56.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.082</td> <th>  Prob(JB):          </th> <td>4.33e-13</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.354</td> <th>  Cond. No.          </th> <td>    3.73</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/latex": [
       "\\begin{center}\n",
       "\\begin{tabular}{lclc}\n",
       "\\toprule\n",
       "\\textbf{Dep. Variable:}    &        y         & \\textbf{  R-squared:         } &     0.003   \\\\\n",
       "\\textbf{Model:}            &       OLS        & \\textbf{  Adj. R-squared:    } &     0.003   \\\\\n",
       "\\textbf{Method:}           &  Least Squares   & \\textbf{  F-statistic:       } &     12.78   \\\\\n",
       "\\textbf{Date:}             & Thu, 15 Aug 2024 & \\textbf{  Prob (F-statistic):} &  2.86e-06   \\\\\n",
       "\\textbf{Time:}             &     10:19:45     & \\textbf{  Log-Likelihood:    } &   -12757.   \\\\\n",
       "\\textbf{No. Observations:} &        9000      & \\textbf{  AIC:               } & 2.552e+04   \\\\\n",
       "\\textbf{Df Residuals:}     &        8997      & \\textbf{  BIC:               } & 2.554e+04   \\\\\n",
       "\\textbf{Df Model:}         &           2      & \\textbf{                     } &             \\\\\n",
       "\\textbf{Covariance Type:}  &    nonrobust     & \\textbf{                     } &             \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lcccccc}\n",
       "               & \\textbf{coef} & \\textbf{std err} & \\textbf{t} & \\textbf{P$> |$t$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n",
       "\\midrule\n",
       "\\textbf{const} &      -0.0244  &        0.018     &    -1.340  &         0.180        &       -0.060    &        0.011     \\\\\n",
       "\\textbf{2}     &       0.0983  &        0.026     &     3.812  &         0.000        &        0.048    &        0.149     \\\\\n",
       "\\textbf{3}     &      -0.0250  &        0.026     &    -0.970  &         0.332        &       -0.076    &        0.026     \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "\\begin{tabular}{lclc}\n",
       "\\textbf{Omnibus:}       & 44.697 & \\textbf{  Durbin-Watson:     } &    0.542  \\\\\n",
       "\\textbf{Prob(Omnibus):} &  0.000 & \\textbf{  Jarque-Bera (JB):  } &   56.937  \\\\\n",
       "\\textbf{Skew:}          & -0.082 & \\textbf{  Prob(JB):          } & 4.33e-13  \\\\\n",
       "\\textbf{Kurtosis:}      &  3.354 & \\textbf{  Cond. No.          } &     3.73  \\\\\n",
       "\\bottomrule\n",
       "\\end{tabular}\n",
       "%\\caption{OLS Regression Results}\n",
       "\\end{center}\n",
       "\n",
       "Notes: \\newline\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                      y   R-squared:                       0.003\n",
       "Model:                            OLS   Adj. R-squared:                  0.003\n",
       "Method:                 Least Squares   F-statistic:                     12.78\n",
       "Date:                Thu, 15 Aug 2024   Prob (F-statistic):           2.86e-06\n",
       "Time:                        10:19:45   Log-Likelihood:                -12757.\n",
       "No. Observations:                9000   AIC:                         2.552e+04\n",
       "Df Residuals:                    8997   BIC:                         2.554e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         -0.0244      0.018     -1.340      0.180      -0.060       0.011\n",
       "2              0.0983      0.026      3.812      0.000       0.048       0.149\n",
       "3             -0.0250      0.026     -0.970      0.332      -0.076       0.026\n",
       "==============================================================================\n",
       "Omnibus:                       44.697   Durbin-Watson:                   0.542\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               56.937\n",
       "Skew:                          -0.082   Prob(JB):                     4.33e-13\n",
       "Kurtosis:                       3.354   Cond. No.                         3.73\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# doing the regressions like in Javdani and Chang (2023)\n",
    "import statsmodels.api as sm\n",
    "y=pd.concat([df['claimquestionanswergpt35clean'],df['claimquestionanswergpt4oclean'], df['claimquestionanswergpt4oprofclean']], axis=0).reset_index(drop=True)\n",
    "y=y.replace('strongly agree',5)\n",
    "y=y.replace('agree',4)\n",
    "y=y.replace('neutral',3)\n",
    "y=y.replace('disagree',2)\n",
    "y=y.replace('strongly disagree',1)\n",
    "y.describe()\n",
    "\n",
    "x=np.repeat([1,2,3],3000)\n",
    "x=pd.get_dummies(x,drop_first=True)*1\n",
    "x = sm.add_constant(x)\n",
    "model = sm.OLS(y,x)\n",
    "results = model.fit()\n",
    "display(results.summary())\n",
    "\n",
    "y=(y-y.mean())/y.std()\n",
    "y.describe()\n",
    "\n",
    "x=np.repeat([1,2,3],3000)\n",
    "x=pd.get_dummies(x,drop_first=True)*1\n",
    "x = sm.add_constant(x)\n",
    "model = sm.OLS(y,x)\n",
    "results = model.fit()\n",
    "display(results.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "07ddc2c1-7d4c-4435-9632-73bf94126208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_8840a caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_8840a td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_8840a th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_8840a .true {\n",
       "  border-right: solid;\n",
       "  border-color: blue;\n",
       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_8840a\">\n",
       "  <caption>Table A4 - 15 Claims from Javdani and Chang (2023)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8840a_level0_col0\" class=\"col_heading level0 col0\" >Claims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row0\" class=\"row_heading level0 row0\" >Tone-deaf</th>\n",
       "      <td id=\"T_8840a_row0_col0\" class=\"data row0 col0\" >“When we expect redistributive effects to even out in the long run, so that everyone eventually comes out ahead, we are more likely to overlook reshufflings of income. That is a key reason why we believe that technological progress should run its course, despite its short-run destructive effects on some. When, on the other hand, the forces of trade repeatedly hit the same people – less educated, blue-collar workers – we may feel less sanguine about globalization. Too many economists are tone-deaf to such distinctions. They are prone to attribute concerns about globalization to crass protectionist motives or ignorance, even when there are genuine ethical issues at stake.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row1\" class=\"row_heading level0 row1\" >Intellectual monopoly</th>\n",
       "      <td id=\"T_8840a_row1_col0\" class=\"data row1 col0\" >“A realistic view of intellectual monopoly [e.g. patent, copyright] is that it is a disease rather than a cure. It arises not from a principled effort to increase innovation, but from an evil combination of medieval institutions – guilds, royal licenses, trade restrictions – and the rent-seeking behaviour of would be monopolists seeking to fatten their purse at the expense of public prosperity.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row2\" class=\"row_heading level0 row2\" >Non-rational</th>\n",
       "      <td id=\"T_8840a_row2_col0\" class=\"data row2 col0\" >“It is only in combination with particular, non-rational impulses that reason can determine what to do…”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row3\" class=\"row_heading level0 row3\" >The very wealthy</th>\n",
       "      <td id=\"T_8840a_row3_col0\" class=\"data row3 col0\" >“The very wealthy have little need for state-provided education or health care; they have every reason to support cuts in Medicare and to fight any increase in taxes. They have even less reason to support health insurance for everyone, or to worry about the low quality of public schools that plagues much of the country. They will oppose any regulation of banks that restricts profits, even if it helps those who cannot cover their mortgages or protects the public against predatory lending, deceptive advertising, or even a repetition of the financial crash.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row4\" class=\"row_heading level0 row4\" >Gender-gap</th>\n",
       "      <td id=\"T_8840a_row4_col0\" class=\"data row4 col0\" >“Unlike most other science and social science disciplines, economics has made little progress in closing its gender gap over the last several decades. Given the field’s prominence in determining public policy, this is a serious issue. Whether explicit or more subtle, intentional or not, the hurdles that women face in economics are very real.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row5\" class=\"row_heading level0 row5\" >Rhetoric</th>\n",
       "      <td id=\"T_8840a_row5_col0\" class=\"data row5 col0\" >“Economic discourse of any sort - verbal, mathematical, econometric-is rhetoric; that is, an effort to persuade. None of these discursive forms should necessarily be privileged over the others unless it is agreed by the community of scholars to be more compelling. Only when economists move away from the pursuit of universal knowledge of 'the economy' and towards an acceptance of the necessity of vision and the historical and spatial contingency of knowledge will the concern over ideological 'bias' begin to fade. Such a turn would have important implications for economic method as well, as knowledge claims would increasingly find support, not in models of constrained optimization, but with such techniques as case studies and historical analyses of social institutions and politics. Increasing reliance of economics on mathematics and statistics has not freed the discipline from ideological bias, it has simply made it easier to disregard.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row6\" class=\"row_heading level0 row6\" >Bias</th>\n",
       "      <td id=\"T_8840a_row6_col0\" class=\"data row6 col0\" >“Academic economists, from their very open-mindedness, are apt to be carried off, unawares, by the bias of the community in which they live. Economists whose social world is Wall Street are very apt to take the Wall Street point of view, while economists at state universities situated in farming districts are apt to be partisans of the agricultural interests\".</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row7\" class=\"row_heading level0 row7\" >Market Economy</th>\n",
       "      <td id=\"T_8840a_row7_col0\" class=\"data row7 col0\" >“The market economy has depended for its own working not only on maximizing profits but also on many other activities, such as maintaining public security and supplying public services—some of which have taken people well beyond an economy driven only by profit. The creditable performance of the so-called capitalist system, when things moved forward, drew on a combination of institutions that went much beyond relying only on a profit-maximizing market economy and on personal entitlements confined to private ownership.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row8\" class=\"row_heading level0 row8\" >Unfair</th>\n",
       "      <td id=\"T_8840a_row8_col0\" class=\"data row8 col0\" >“The laws of property have made property of things which never ought to be property, and absolute property where only a qualified property ought to exist. They have not held the balance fairly between human beings, but have heaped impediments upon some, to give advantage to others; they have purposely fostered inequalities, and prevented all from starting fair in the race.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row9\" class=\"row_heading level0 row9\" >Capitalism</th>\n",
       "      <td id=\"T_8840a_row9_col0\" class=\"data row9 col0\" >“Sharp increases in unemployment beyond the business cycle—one in six American men between 25 and 54 are likely to be out of work even after the U.S. economy recovers—along with dramatic rises in the share of income going to the top 1 and even the top .01 per cent of the population and declining social mobility do raise serious questions about the fairness of capitalism...”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row10\" class=\"row_heading level0 row10\" >Psuedo-mathematical</th>\n",
       "      <td id=\"T_8840a_row10_col0\" class=\"data row10 col0\" >“It is a great fault of symbolic pseudo-mathematical methods of formalizing a system of economic analysis…that they expressly assume strict independence between the factors involved and lose all their cogency and authority if this hypothesis is disallowed; … Too large a proportion of recent mathematical economics are mere concoctions, as imprecise as the initial assumptions they rest upon, which allow the author to lose sight of the complexities and interdependencies of the real world in a maze of pretentious and unhelpful symbols.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row11\" class=\"row_heading level0 row11\" >Behavioral Economics</th>\n",
       "      <td id=\"T_8840a_row11_col0\" class=\"data row11 col0\" >“From this failure to expunge the microeconomic foundations of neoclassical economics from post-Great Depression theory arose the \"microfoundations of macroeconomics\" debate, which ultimately led to a model in which the economy is viewed as a single utility-maximizing individual blessed with perfect knowledge of the future. Fortunately, behavioral economics provides the beginnings of an alternative vision of how individuals operate in a market environment, while multi-agent modelling and network theory give us foundations for understanding group dynamics in a complex society. […] These approaches should replace neoclassical microeconomics completely.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row12\" class=\"row_heading level0 row12\" >Adam Smith</th>\n",
       "      <td id=\"T_8840a_row12_col0\" class=\"data row12 col0\" >“In the progress of the division of labour, the employment of the far greater part of those who live by labour, that is, of the great body of people, comes to be confined to a few very simple operations, frequently one or two. But the understandings of the greater part of men are necessarily formed by their ordinary employments. The man whose whole life is spent in performing a few simple operations, of which the effects too are, perhaps, always the same, or very nearly the same, has no occasion to exert his understanding, or to exercise his invention in finding out expedients for removing difficulties which never occur. He naturally loses, therefore, the habit of such exertion, and generally becomes as stupid and ignorant as it is possible for a human creature to become.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row13\" class=\"row_heading level0 row13\" >Bad Predictions</th>\n",
       "      <td id=\"T_8840a_row13_col0\" class=\"data row13 col0\" >“For four decades, since my time as a graduate student, I have been preoccupied by the kinds of stories about the myriad ways in which people depart from the fictional creatures that populate economic models […]. Compared to this fictional world of Econs, Humans do a lot of misbehaving, and that means that economic models make a lot of bad predictions, predictions that can have much more serious consequences than upsetting a group of students. Virtually no economists saw the financial crisis of 2007–08 coming, and worse, many thought that both the crash and its aftermath were things that simply could not happen.”</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8840a_level0_row14\" class=\"row_heading level0 row14\" >Think alike</th>\n",
       "      <td id=\"T_8840a_row14_col0\" class=\"data row14 col0\" >“There are powerful forces having to do with the sociology of the profession and the socialization process that tend to push economists to think alike. Most economists start graduate school not having spent much time thinking about social problems or having studied much else besides math and economics. The incentive and hierarchy systems tend to reward those with the technical skills rather than interesting questions or research agendas. An in-group versus out-group mentality develops rather early on that pits economists against other social scientists. […] [E]conomists tend to look down on other social scientists, as those distant, less competent cousins who may ask interesting questions sometimes but never get the answers right. Or, if their answers are right, they are so not for the methodologically correct reasons. Even economists who come from different intellectual traditions are typically treated as “not real economists” or “not serious economists.”</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f93643d8640>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_93e22 caption {\n",
       "  text-align: center;\n",
       "  font-size: 150%;\n",
       "  color: blue;\n",
       "}\n",
       "#T_93e22 td {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_93e22 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_93e22 .true {\n",
       "  border-right: solid;\n",
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       "  vertical-align: top;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_93e22\">\n",
       "  <caption>Table A4 - 15 Mainstream Sources from Javdani and Chang (2023)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_93e22_level0_col0\" class=\"col_heading level0 col0\" >Claims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >Name</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row0\" class=\"row_heading level0 row0\" >Tone-deaf</th>\n",
       "      <td id=\"T_93e22_row0_col0\" class=\"data row0 col0\" >Dani Rodrik, Professor of International Political Economy at Harvard University and the author of The Globalization Paradox: Democracy and the Future of the World Economy (2012).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row1\" class=\"row_heading level0 row1\" >Intellectual monopoly</th>\n",
       "      <td id=\"T_93e22_row1_col0\" class=\"data row1 col0\" >David Levine, Professor of Economics at Washington University in St. Louis and the author of Against Intellectual Monopoly (2008).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row2\" class=\"row_heading level0 row2\" >Non-rational</th>\n",
       "      <td id=\"T_93e22_row2_col0\" class=\"data row2 col0\" >Friedrich von Hayek (1899-1992), Professor of Economics at University of Chicago and London School of Economics, and the 1974 recipient of the Nobel Prize in Economics.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row3\" class=\"row_heading level0 row3\" >The very wealthy</th>\n",
       "      <td id=\"T_93e22_row3_col0\" class=\"data row3 col0\" >Angus Deaton, Professor of Economics at Princeton University, the 2015 recipient of the Nobel Prize in Economics, and the author of The Great Escape: Health, Wealth, and the Origins of Inequality (2013).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row4\" class=\"row_heading level0 row4\" >Gender-gap</th>\n",
       "      <td id=\"T_93e22_row4_col0\" class=\"data row4 col0\" >Carmen Reinhart, Professor of the International Financial System at Harvard Kennedy School and the author of This Time is Different: Eight Centuries of Financial Folly (2011).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row5\" class=\"row_heading level0 row5\" >Rhetoric</th>\n",
       "      <td id=\"T_93e22_row5_col0\" class=\"data row5 col0\" >Ronald Coase (1910-2013), Professor of Economics at the University of Chicago Law School and the 1991 recipient of the Nobel Prize in Economics.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row6\" class=\"row_heading level0 row6\" >Bias</th>\n",
       "      <td id=\"T_93e22_row6_col0\" class=\"data row6 col0\" >Irving Fisher (1867-1947), Professor of Political Economy at Yale University.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row7\" class=\"row_heading level0 row7\" >Market Economy</th>\n",
       "      <td id=\"T_93e22_row7_col0\" class=\"data row7 col0\" >Amartya Sen, Professor of Economics and Philosophy at Harvard University and the author of Development as Freedom (1999).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row8\" class=\"row_heading level0 row8\" >Unfair</th>\n",
       "      <td id=\"T_93e22_row8_col0\" class=\"data row8 col0\" >John Stuart Mill (1806-1873), an English philosopher, political economist, and the author of On Liberty (1859).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row9\" class=\"row_heading level0 row9\" >Capitalism</th>\n",
       "      <td id=\"T_93e22_row9_col0\" class=\"data row9 col0\" >Larry Summers, Professor of Economics and President Emeritus at Harvard University.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row10\" class=\"row_heading level0 row10\" >Psuedo-mathematical</th>\n",
       "      <td id=\"T_93e22_row10_col0\" class=\"data row10 col0\" >Kenneth Arrow, Professor of Economics at Stanford University and the 1972 recipient of the Nobel Prize in Economics.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row11\" class=\"row_heading level0 row11\" >Behavioral Economics</th>\n",
       "      <td id=\"T_93e22_row11_col0\" class=\"data row11 col0\" >Paul Romer, Professor of Economics at New York University and the author of The Troubles with Macroeconomics (forthcoming in the American Economic Review).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row12\" class=\"row_heading level0 row12\" >Adam Smith</th>\n",
       "      <td id=\"T_93e22_row12_col0\" class=\"data row12 col0\" >Adam Smith.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row13\" class=\"row_heading level0 row13\" >Bad Predictions</th>\n",
       "      <td id=\"T_93e22_row13_col0\" class=\"data row13 col0\" >Richard Thaler, Professor of Behavioural Science and Economics at University of Chicago Booth School of Business and the author of Misbehaving: The Making of Behavioural Economics (2015).</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_93e22_level0_row14\" class=\"row_heading level0 row14\" >Think alike</th>\n",
       "      <td id=\"T_93e22_row14_col0\" class=\"data row14 col0\" >Dani Rodrik, Professor of International Political Economy at Harvard University and the author of The Globalization Paradox: Democracy and the Future of the World Economy (2012).</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f93643d8eb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: This table is based on 15 survey questions from Javdani and Chang (2023).\n"
     ]
    }
   ],
   "source": [
    "claimform10a=realaltered['statement'].rename('Claims').to_frame()\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='Tone-deaf'\n",
    "claimform10a.at[1,'Name']='Intellectual monopoly'\n",
    "claimform10a.at[2,'Name']='Non-rational'\n",
    "claimform10a.at[3,'Name']='The very wealthy'\n",
    "claimform10a.at[4,'Name']='Gender-gap'\n",
    "claimform10a.at[5,'Name']='Rhetoric'\n",
    "claimform10a.at[6,'Name']='Bias'\n",
    "claimform10a.at[7,'Name']='Market Economy'\n",
    "claimform10a.at[8,'Name']='Unfair'\n",
    "claimform10a.at[9,'Name']='Capitalism'\n",
    "claimform10a.at[10,'Name']='Psuedo-mathematical'\n",
    "claimform10a.at[11,'Name']='Behavioral Economics'\n",
    "claimform10a.at[12,'Name']='Adam Smith'\n",
    "claimform10a.at[13,'Name']='Bad Predictions'\n",
    "claimform10a.at[14,'Name']='Think alike'\n",
    "\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "display(claimform10a.style.set_caption(\"Table A4 - 15 Claims from Javdani and Chang (2023)\").set_table_styles(styles))\n",
    "\n",
    "\n",
    "claimform10a=realaltered['mainstream'].rename('Claims').to_frame()\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='Tone-deaf'\n",
    "claimform10a.at[1,'Name']='Intellectual monopoly'\n",
    "claimform10a.at[2,'Name']='Non-rational'\n",
    "claimform10a.at[3,'Name']='The very wealthy'\n",
    "claimform10a.at[4,'Name']='Gender-gap'\n",
    "claimform10a.at[5,'Name']='Rhetoric'\n",
    "claimform10a.at[6,'Name']='Bias'\n",
    "claimform10a.at[7,'Name']='Market Economy'\n",
    "claimform10a.at[8,'Name']='Unfair'\n",
    "claimform10a.at[9,'Name']='Capitalism'\n",
    "claimform10a.at[10,'Name']='Psuedo-mathematical'\n",
    "claimform10a.at[11,'Name']='Behavioral Economics'\n",
    "claimform10a.at[12,'Name']='Adam Smith'\n",
    "claimform10a.at[13,'Name']='Bad Predictions'\n",
    "claimform10a.at[14,'Name']='Think alike'\n",
    "\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "display(claimform10a.style.set_caption(\"Table A4 - 15 Mainstream Sources from Javdani and Chang (2023)\").set_table_styles(styles))\n",
    "\n",
    "\n",
    "claimform10a=realaltered['alternative'].rename('Claims').to_frame()\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='Tone-deaf'\n",
    "claimform10a.at[1,'Name']='Intellectual monopoly'\n",
    "claimform10a.at[2,'Name']='Non-rational'\n",
    "claimform10a.at[3,'Name']='The very wealthy'\n",
    "claimform10a.at[4,'Name']='Gender-gap'\n",
    "claimform10a.at[5,'Name']='Rhetoric'\n",
    "claimform10a.at[6,'Name']='Bias'\n",
    "claimform10a.at[7,'Name']='Market Economy'\n",
    "claimform10a.at[8,'Name']='Unfair'\n",
    "claimform10a.at[9,'Name']='Capitalism'\n",
    "claimform10a.at[10,'Name']='Psuedo-mathematical'\n",
    "claimform10a.at[11,'Name']='Behavioral Economics'\n",
    "claimform10a.at[12,'Name']='Adam Smith'\n",
    "claimform10a.at[13,'Name']='Bad Predictions'\n",
    "claimform10a.at[14,'Name']='Think alike'\n",
    "\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "claimform10a.style.set_caption(\"Table A4 - Economists versus ChatGPT - 15 Less/Non-Mainstream Sources from Javdani and Chang (2023)\").set_table_styles(styles)\n",
    "print('Notes: This table is based on 15 survey questions from Javdani and Chang (2023).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fa22359b-3457-4faa-b604-0e3780909d6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "}\n",
       "#T_b36e0 td {\n",
       "  text-align: center;\n",
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       "</style>\n",
       "<table id=\"T_b36e0\">\n",
       "  <caption>Table 8 - Economists vs ChatGPT4o - statements from Javdani and Chang (2023)</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b36e0_level0_col0\" class=\"col_heading level0 col0\" colspan=\"5\">Economists Survey</th>\n",
       "      <th id=\"T_b36e0_level0_col5\" class=\"col_heading level0 col5\" colspan=\"5\">ChatGPT4o</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"blank level1\" >&nbsp;</th>\n",
       "      <th id=\"T_b36e0_level1_col0\" class=\"col_heading level1 col0\" >strongly disagree</th>\n",
       "      <th id=\"T_b36e0_level1_col1\" class=\"col_heading level1 col1\" >disagree</th>\n",
       "      <th id=\"T_b36e0_level1_col2\" class=\"col_heading level1 col2\" >neutral</th>\n",
       "      <th id=\"T_b36e0_level1_col3\" class=\"col_heading level1 col3\" >agree</th>\n",
       "      <th id=\"T_b36e0_level1_col4\" class=\"col_heading level1 col4\" >strongly agree</th>\n",
       "      <th id=\"T_b36e0_level1_col5\" class=\"col_heading level1 col5\" >strongly disagree</th>\n",
       "      <th id=\"T_b36e0_level1_col6\" class=\"col_heading level1 col6\" >disagree</th>\n",
       "      <th id=\"T_b36e0_level1_col7\" class=\"col_heading level1 col7\" >neutral</th>\n",
       "      <th id=\"T_b36e0_level1_col8\" class=\"col_heading level1 col8\" >agree</th>\n",
       "      <th id=\"T_b36e0_level1_col9\" class=\"col_heading level1 col9\" >strongly agree</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row0\" class=\"row_heading level0 row0\" >Tone-deaf</th>\n",
       "      <td id=\"T_b36e0_row0_col0\" class=\"data row0 col0\" >3.0</td>\n",
       "      <td id=\"T_b36e0_row0_col1\" class=\"data row0 col1\" >14.0</td>\n",
       "      <td id=\"T_b36e0_row0_col2\" class=\"data row0 col2\" >14.0</td>\n",
       "      <td id=\"T_b36e0_row0_col3\" class=\"data row0 col3\" >54.0</td>\n",
       "      <td id=\"T_b36e0_row0_col4\" class=\"data row0 col4\" >13.0</td>\n",
       "      <td id=\"T_b36e0_row0_col5\" class=\"data row0 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row0_col6\" class=\"data row0 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row0_col7\" class=\"data row0 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row0_col8\" class=\"data row0 col8\" >89.0</td>\n",
       "      <td id=\"T_b36e0_row0_col9\" class=\"data row0 col9\" >11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row1\" class=\"row_heading level0 row1\" >Intellectual monopoly</th>\n",
       "      <td id=\"T_b36e0_row1_col0\" class=\"data row1 col0\" >14.0</td>\n",
       "      <td id=\"T_b36e0_row1_col1\" class=\"data row1 col1\" >45.0</td>\n",
       "      <td id=\"T_b36e0_row1_col2\" class=\"data row1 col2\" >18.0</td>\n",
       "      <td id=\"T_b36e0_row1_col3\" class=\"data row1 col3\" >19.0</td>\n",
       "      <td id=\"T_b36e0_row1_col4\" class=\"data row1 col4\" >5.0</td>\n",
       "      <td id=\"T_b36e0_row1_col5\" class=\"data row1 col5\" >2.5</td>\n",
       "      <td id=\"T_b36e0_row1_col6\" class=\"data row1 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row1_col7\" class=\"data row1 col7\" >97.5</td>\n",
       "      <td id=\"T_b36e0_row1_col8\" class=\"data row1 col8\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row1_col9\" class=\"data row1 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row2\" class=\"row_heading level0 row2\" >Non-rational</th>\n",
       "      <td id=\"T_b36e0_row2_col0\" class=\"data row2 col0\" >5.0</td>\n",
       "      <td id=\"T_b36e0_row2_col1\" class=\"data row2 col1\" >20.0</td>\n",
       "      <td id=\"T_b36e0_row2_col2\" class=\"data row2 col2\" >32.0</td>\n",
       "      <td id=\"T_b36e0_row2_col3\" class=\"data row2 col3\" >34.0</td>\n",
       "      <td id=\"T_b36e0_row2_col4\" class=\"data row2 col4\" >9.0</td>\n",
       "      <td id=\"T_b36e0_row2_col5\" class=\"data row2 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row2_col6\" class=\"data row2 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row2_col7\" class=\"data row2 col7\" >1.0</td>\n",
       "      <td id=\"T_b36e0_row2_col8\" class=\"data row2 col8\" >98.5</td>\n",
       "      <td id=\"T_b36e0_row2_col9\" class=\"data row2 col9\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row3\" class=\"row_heading level0 row3\" >The very wealthy</th>\n",
       "      <td id=\"T_b36e0_row3_col0\" class=\"data row3 col0\" >8.0</td>\n",
       "      <td id=\"T_b36e0_row3_col1\" class=\"data row3 col1\" >23.0</td>\n",
       "      <td id=\"T_b36e0_row3_col2\" class=\"data row3 col2\" >10.0</td>\n",
       "      <td id=\"T_b36e0_row3_col3\" class=\"data row3 col3\" >39.0</td>\n",
       "      <td id=\"T_b36e0_row3_col4\" class=\"data row3 col4\" >20.0</td>\n",
       "      <td id=\"T_b36e0_row3_col5\" class=\"data row3 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row3_col6\" class=\"data row3 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row3_col7\" class=\"data row3 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row3_col8\" class=\"data row3 col8\" >91.5</td>\n",
       "      <td id=\"T_b36e0_row3_col9\" class=\"data row3 col9\" >8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row4\" class=\"row_heading level0 row4\" >Gender-gap</th>\n",
       "      <td id=\"T_b36e0_row4_col0\" class=\"data row4 col0\" >7.0</td>\n",
       "      <td id=\"T_b36e0_row4_col1\" class=\"data row4 col1\" >19.0</td>\n",
       "      <td id=\"T_b36e0_row4_col2\" class=\"data row4 col2\" >16.0</td>\n",
       "      <td id=\"T_b36e0_row4_col3\" class=\"data row4 col3\" >36.0</td>\n",
       "      <td id=\"T_b36e0_row4_col4\" class=\"data row4 col4\" >21.0</td>\n",
       "      <td id=\"T_b36e0_row4_col5\" class=\"data row4 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row4_col6\" class=\"data row4 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row4_col7\" class=\"data row4 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row4_col8\" class=\"data row4 col8\" >11.0</td>\n",
       "      <td id=\"T_b36e0_row4_col9\" class=\"data row4 col9\" >89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row5\" class=\"row_heading level0 row5\" >Rhetoric</th>\n",
       "      <td id=\"T_b36e0_row5_col0\" class=\"data row5 col0\" >9.0</td>\n",
       "      <td id=\"T_b36e0_row5_col1\" class=\"data row5 col1\" >22.0</td>\n",
       "      <td id=\"T_b36e0_row5_col2\" class=\"data row5 col2\" >17.0</td>\n",
       "      <td id=\"T_b36e0_row5_col3\" class=\"data row5 col3\" >35.0</td>\n",
       "      <td id=\"T_b36e0_row5_col4\" class=\"data row5 col4\" >17.0</td>\n",
       "      <td id=\"T_b36e0_row5_col5\" class=\"data row5 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row5_col6\" class=\"data row5 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row5_col7\" class=\"data row5 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row5_col8\" class=\"data row5 col8\" >95.5</td>\n",
       "      <td id=\"T_b36e0_row5_col9\" class=\"data row5 col9\" >4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row6\" class=\"row_heading level0 row6\" >Bias</th>\n",
       "      <td id=\"T_b36e0_row6_col0\" class=\"data row6 col0\" >3.0</td>\n",
       "      <td id=\"T_b36e0_row6_col1\" class=\"data row6 col1\" >20.0</td>\n",
       "      <td id=\"T_b36e0_row6_col2\" class=\"data row6 col2\" >22.0</td>\n",
       "      <td id=\"T_b36e0_row6_col3\" class=\"data row6 col3\" >45.0</td>\n",
       "      <td id=\"T_b36e0_row6_col4\" class=\"data row6 col4\" >9.0</td>\n",
       "      <td id=\"T_b36e0_row6_col5\" class=\"data row6 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row6_col6\" class=\"data row6 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row6_col7\" class=\"data row6 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row6_col8\" class=\"data row6 col8\" >99.0</td>\n",
       "      <td id=\"T_b36e0_row6_col9\" class=\"data row6 col9\" >1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row7\" class=\"row_heading level0 row7\" >Market Economy</th>\n",
       "      <td id=\"T_b36e0_row7_col0\" class=\"data row7 col0\" >1.0</td>\n",
       "      <td id=\"T_b36e0_row7_col1\" class=\"data row7 col1\" >4.0</td>\n",
       "      <td id=\"T_b36e0_row7_col2\" class=\"data row7 col2\" >13.0</td>\n",
       "      <td id=\"T_b36e0_row7_col3\" class=\"data row7 col3\" >48.0</td>\n",
       "      <td id=\"T_b36e0_row7_col4\" class=\"data row7 col4\" >35.0</td>\n",
       "      <td id=\"T_b36e0_row7_col5\" class=\"data row7 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row7_col6\" class=\"data row7 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row7_col7\" class=\"data row7 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row7_col8\" class=\"data row7 col8\" >20.0</td>\n",
       "      <td id=\"T_b36e0_row7_col9\" class=\"data row7 col9\" >80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row8\" class=\"row_heading level0 row8\" >Unfair</th>\n",
       "      <td id=\"T_b36e0_row8_col0\" class=\"data row8 col0\" >9.0</td>\n",
       "      <td id=\"T_b36e0_row8_col1\" class=\"data row8 col1\" >25.0</td>\n",
       "      <td id=\"T_b36e0_row8_col2\" class=\"data row8 col2\" >23.0</td>\n",
       "      <td id=\"T_b36e0_row8_col3\" class=\"data row8 col3\" >31.0</td>\n",
       "      <td id=\"T_b36e0_row8_col4\" class=\"data row8 col4\" >12.0</td>\n",
       "      <td id=\"T_b36e0_row8_col5\" class=\"data row8 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row8_col6\" class=\"data row8 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row8_col7\" class=\"data row8 col7\" >45.0</td>\n",
       "      <td id=\"T_b36e0_row8_col8\" class=\"data row8 col8\" >55.0</td>\n",
       "      <td id=\"T_b36e0_row8_col9\" class=\"data row8 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row9\" class=\"row_heading level0 row9\" >Capitalism</th>\n",
       "      <td id=\"T_b36e0_row9_col0\" class=\"data row9 col0\" >6.0</td>\n",
       "      <td id=\"T_b36e0_row9_col1\" class=\"data row9 col1\" >13.0</td>\n",
       "      <td id=\"T_b36e0_row9_col2\" class=\"data row9 col2\" >12.0</td>\n",
       "      <td id=\"T_b36e0_row9_col3\" class=\"data row9 col3\" >40.0</td>\n",
       "      <td id=\"T_b36e0_row9_col4\" class=\"data row9 col4\" >29.0</td>\n",
       "      <td id=\"T_b36e0_row9_col5\" class=\"data row9 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row9_col6\" class=\"data row9 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row9_col7\" class=\"data row9 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row9_col8\" class=\"data row9 col8\" >95.0</td>\n",
       "      <td id=\"T_b36e0_row9_col9\" class=\"data row9 col9\" >5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row10\" class=\"row_heading level0 row10\" >Psuedo-mathematical</th>\n",
       "      <td id=\"T_b36e0_row10_col0\" class=\"data row10 col0\" >8.0</td>\n",
       "      <td id=\"T_b36e0_row10_col1\" class=\"data row10 col1\" >19.0</td>\n",
       "      <td id=\"T_b36e0_row10_col2\" class=\"data row10 col2\" >16.0</td>\n",
       "      <td id=\"T_b36e0_row10_col3\" class=\"data row10 col3\" >35.0</td>\n",
       "      <td id=\"T_b36e0_row10_col4\" class=\"data row10 col4\" >22.0</td>\n",
       "      <td id=\"T_b36e0_row10_col5\" class=\"data row10 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row10_col6\" class=\"data row10 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row10_col7\" class=\"data row10 col7\" >4.0</td>\n",
       "      <td id=\"T_b36e0_row10_col8\" class=\"data row10 col8\" >96.0</td>\n",
       "      <td id=\"T_b36e0_row10_col9\" class=\"data row10 col9\" >0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row11\" class=\"row_heading level0 row11\" >Behavioral Economics</th>\n",
       "      <td id=\"T_b36e0_row11_col0\" class=\"data row11 col0\" >12.0</td>\n",
       "      <td id=\"T_b36e0_row11_col1\" class=\"data row11 col1\" >27.0</td>\n",
       "      <td id=\"T_b36e0_row11_col2\" class=\"data row11 col2\" >24.0</td>\n",
       "      <td id=\"T_b36e0_row11_col3\" class=\"data row11 col3\" >29.0</td>\n",
       "      <td id=\"T_b36e0_row11_col4\" class=\"data row11 col4\" >8.0</td>\n",
       "      <td id=\"T_b36e0_row11_col5\" class=\"data row11 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row11_col6\" class=\"data row11 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row11_col7\" class=\"data row11 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row11_col8\" class=\"data row11 col8\" >98.0</td>\n",
       "      <td id=\"T_b36e0_row11_col9\" class=\"data row11 col9\" >2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row12\" class=\"row_heading level0 row12\" >Adam Smith</th>\n",
       "      <td id=\"T_b36e0_row12_col0\" class=\"data row12 col0\" >10.0</td>\n",
       "      <td id=\"T_b36e0_row12_col1\" class=\"data row12 col1\" >31.0</td>\n",
       "      <td id=\"T_b36e0_row12_col2\" class=\"data row12 col2\" >25.0</td>\n",
       "      <td id=\"T_b36e0_row12_col3\" class=\"data row12 col3\" >27.0</td>\n",
       "      <td id=\"T_b36e0_row12_col4\" class=\"data row12 col4\" >7.0</td>\n",
       "      <td id=\"T_b36e0_row12_col5\" class=\"data row12 col5\" >1.0</td>\n",
       "      <td id=\"T_b36e0_row12_col6\" class=\"data row12 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row12_col7\" class=\"data row12 col7\" >32.5</td>\n",
       "      <td id=\"T_b36e0_row12_col8\" class=\"data row12 col8\" >66.0</td>\n",
       "      <td id=\"T_b36e0_row12_col9\" class=\"data row12 col9\" >0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row13\" class=\"row_heading level0 row13\" >Bad Predictions</th>\n",
       "      <td id=\"T_b36e0_row13_col0\" class=\"data row13 col0\" >7.0</td>\n",
       "      <td id=\"T_b36e0_row13_col1\" class=\"data row13 col1\" >20.0</td>\n",
       "      <td id=\"T_b36e0_row13_col2\" class=\"data row13 col2\" >17.0</td>\n",
       "      <td id=\"T_b36e0_row13_col3\" class=\"data row13 col3\" >40.0</td>\n",
       "      <td id=\"T_b36e0_row13_col4\" class=\"data row13 col4\" >16.0</td>\n",
       "      <td id=\"T_b36e0_row13_col5\" class=\"data row13 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row13_col6\" class=\"data row13 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row13_col7\" class=\"data row13 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row13_col8\" class=\"data row13 col8\" >40.0</td>\n",
       "      <td id=\"T_b36e0_row13_col9\" class=\"data row13 col9\" >60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row14\" class=\"row_heading level0 row14\" >Think alike</th>\n",
       "      <td id=\"T_b36e0_row14_col0\" class=\"data row14 col0\" >3.0</td>\n",
       "      <td id=\"T_b36e0_row14_col1\" class=\"data row14 col1\" >12.0</td>\n",
       "      <td id=\"T_b36e0_row14_col2\" class=\"data row14 col2\" >13.0</td>\n",
       "      <td id=\"T_b36e0_row14_col3\" class=\"data row14 col3\" >45.0</td>\n",
       "      <td id=\"T_b36e0_row14_col4\" class=\"data row14 col4\" >27.0</td>\n",
       "      <td id=\"T_b36e0_row14_col5\" class=\"data row14 col5\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row14_col6\" class=\"data row14 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row14_col7\" class=\"data row14 col7\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row14_col8\" class=\"data row14 col8\" >97.0</td>\n",
       "      <td id=\"T_b36e0_row14_col9\" class=\"data row14 col9\" >3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b36e0_level0_row15\" class=\"row_heading level0 row15\" >mean</th>\n",
       "      <td id=\"T_b36e0_row15_col0\" class=\"data row15 col0\" >7.0</td>\n",
       "      <td id=\"T_b36e0_row15_col1\" class=\"data row15 col1\" >20.9</td>\n",
       "      <td id=\"T_b36e0_row15_col2\" class=\"data row15 col2\" >18.1</td>\n",
       "      <td id=\"T_b36e0_row15_col3\" class=\"data row15 col3\" >37.1</td>\n",
       "      <td id=\"T_b36e0_row15_col4\" class=\"data row15 col4\" >16.7</td>\n",
       "      <td id=\"T_b36e0_row15_col5\" class=\"data row15 col5\" >0.2</td>\n",
       "      <td id=\"T_b36e0_row15_col6\" class=\"data row15 col6\" >0.0</td>\n",
       "      <td id=\"T_b36e0_row15_col7\" class=\"data row15 col7\" >12.0</td>\n",
       "      <td id=\"T_b36e0_row15_col8\" class=\"data row15 col8\" >70.1</td>\n",
       "      <td id=\"T_b36e0_row15_col9\" class=\"data row15 col9\" >17.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f9383181820>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: This table is based on 15 survey questions from Javdani and Chang (2023).\n"
     ]
    }
   ],
   "source": [
    "b=realaltered[['strongly disagree', 'disagree', 'neutral', 'agree', 'strongly agree']]*100\n",
    "a=realaltered[['ChatGPT disagree', 'ChatGPT strongly disagree', 'ChatGPT neutral', 'ChatGPT agree', 'ChatGPT strongly agree']]*100\n",
    "a.columns=b.columns\n",
    "b=pd.concat([b], keys=['Economists Survey'], axis=1)\n",
    "a=pd.concat([a], keys=['ChatGPT4o'], axis=1)\n",
    "\n",
    "\n",
    "\n",
    "claimform10a=a\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='Tone-deaf'\n",
    "claimform10a.at[1,'Name']='Intellectual monopoly'\n",
    "claimform10a.at[2,'Name']='Non-rational'\n",
    "claimform10a.at[3,'Name']='The very wealthy'\n",
    "claimform10a.at[4,'Name']='Gender-gap'\n",
    "claimform10a.at[5,'Name']='Rhetoric'\n",
    "claimform10a.at[6,'Name']='Bias'\n",
    "claimform10a.at[7,'Name']='Market Economy'\n",
    "claimform10a.at[8,'Name']='Unfair'\n",
    "claimform10a.at[9,'Name']='Capitalism'\n",
    "claimform10a.at[10,'Name']='Psuedo-mathematical'\n",
    "claimform10a.at[11,'Name']='Behavioral Economics'\n",
    "claimform10a.at[12,'Name']='Adam Smith'\n",
    "claimform10a.at[13,'Name']='Bad Predictions'\n",
    "claimform10a.at[14,'Name']='Think alike'\n",
    "\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "claimform10aa=claimform10a\n",
    "claimform10aa=pd.concat([claimform10aa, claimform10aa.describe().loc[['mean']]], axis=0)\n",
    "\n",
    "claimform10a=b\n",
    "claimform10a['Name']=None\n",
    "claimform10a.at[0,'Name']='Tone-deaf'\n",
    "claimform10a.at[1,'Name']='Intellectual monopoly'\n",
    "claimform10a.at[2,'Name']='Non-rational'\n",
    "claimform10a.at[3,'Name']='The very wealthy'\n",
    "claimform10a.at[4,'Name']='Gender-gap'\n",
    "claimform10a.at[5,'Name']='Rhetoric'\n",
    "claimform10a.at[6,'Name']='Bias'\n",
    "claimform10a.at[7,'Name']='Market Economy'\n",
    "claimform10a.at[8,'Name']='Unfair'\n",
    "claimform10a.at[9,'Name']='Capitalism'\n",
    "claimform10a.at[10,'Name']='Psuedo-mathematical'\n",
    "claimform10a.at[11,'Name']='Behavioral Economics'\n",
    "claimform10a.at[12,'Name']='Adam Smith'\n",
    "claimform10a.at[13,'Name']='Bad Predictions'\n",
    "claimform10a.at[14,'Name']='Think alike'\n",
    "\n",
    "claimform10a=claimform10a.set_index('Name', drop=True)\n",
    "\n",
    "claimform10ab=claimform10a\n",
    "claimform10ab=pd.concat([claimform10ab, claimform10ab.describe().loc[['mean']]], axis=0)\n",
    "\n",
    "display(pd.concat([claimform10ab,claimform10aa], axis=1).style.set_caption(\"Table 8 - Economists vs ChatGPT4o - statements from Javdani and Chang (2023)\").set_table_styles(styles).format(precision=1))\n",
    "print('Notes: This table is based on 15 survey questions from Javdani and Chang (2023).')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f1fbb05-a6a7-4bd6-8eaf-433b14ef9cff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    }
   ],
   "source": [
    "# the above is for defaukt chatgpt, here i generate the dataset for the economics persona\n",
    "# side issue {'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "# I ask ChatGPT what are the parameters used by the web version of chatgpt\n",
    "from openai import OpenAI\n",
    "import time\n",
    "\n",
    "client = OpenAI(api_key=api_key)\n",
    "#claimtext= 'Antitrust investigations of the dominant firms in artificial intelligence are likely to lead to substantially lower prices of AI products and services for businesses and consumers.'\n",
    "#claimtextinterpretation = 'When given the statement:' + claimtext + ', a person answers:' + claimanwswer + 'Do you think this answer means the person strongly disagrees with the statement, disagrees with the statement, is uncertain about the statement, agrees with the statement, or strongly agrees with the statement, or has no opinion about the statement. MAKE SURE your output is one of the categories stated only'\n",
    "\n",
    "n=200\n",
    "# they are all GPT 4o\n",
    "# 35 is without source attribution\n",
    "# 4o is with mainstream source attribution\n",
    "# 4oProf is with less/non-mainstream source attribution\n",
    "\n",
    "claimquestionanswergpt35=[]\n",
    "claimquestionanswergpt4o=[]\n",
    "claimquestionanswergpt4oprof=[]\n",
    "for j in range(0,15):\n",
    "    for i in range(0,n):\n",
    "        print(i)\n",
    "\n",
    "        #claimtextquestion= 'express your opinion on this statement:' + realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion=realaltered['statement'].loc[j] + ' Express your opinion on this statement by choosing among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt35+=[completion.choices[0].message.content]\n",
    "\n",
    "        \n",
    "        #claimtextquestion= 'Evaluate the following statement by ' + realaltered['mainstream'].loc[j] +' ' + realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion= 'Express your opinion on the following statement by ' + realaltered['mainstream'].loc[j] +' ' + realaltered['statement'].loc[j]  + ' For your answer choose among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4o+=[completion.choices[0].message.content]\n",
    "    \n",
    "        #claimtextquestion= 'Evaluate the following statement by ' + realaltered['alternative'].loc[j] +' '+ realaltered['statement'].loc[j]  + ' Your answer should choose from one of the following options: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        claimtextquestion= 'Express your opinion on the following statement by ' + realaltered['alternative'].loc[j] +' '+ realaltered['statement'].loc[j]  + ' For your answer choose among: strongly agree, agree, neutral, disagree and strongly disagree. Make sure your output is one of the categories stated only.'\n",
    "        \n",
    "        completion = client.chat.completions.create(\n",
    "            model=\"gpt-4o\",\n",
    "          messages=[{'role': 'system', 'content': 'You are an economics professor who works at a leading university'},\n",
    "            {\"role\": \"user\", \"content\": claimtextquestion}\n",
    "          ]\n",
    "        )\n",
    "        \n",
    "        claimquestionanswergpt4oprof+=[completion.choices[0].message.content]\n",
    "    \n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "\n",
    "df['claimquestionanswergpt4o']=claimquestionanswergpt4o\n",
    "df['claimquestionanswergpt4oprof']=claimquestionanswergpt4oprof\n",
    "df['claimquestionanswergpt35']=claimquestionanswergpt35\n",
    "df['claimtext']=np.repeat(realaltered['statement'].to_list(),n)\n",
    "\n",
    "df.to_pickle('claimsrealaltered090824Prof')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67803d9f-1899-4716-84f0-7aa406b9cf18",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0cd6e074-d51f-45e9-865e-02577ec0f332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Popular Books Advice</th>\n",
       "      <th>Profs Advice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Save 10–15 percent of income regardless of age...</td>\n",
       "      <td>Smooth consumption over time. Low or negative ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Don’t annuitize. Spend to keep real level of w...</td>\n",
       "      <td>Fully annuitize wealth in retirement. If not a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divide savings into mental accounts devoted to...</td>\n",
       "      <td>All wealth is fungible.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hold money that might be spent in short term e...</td>\n",
       "      <td>Invest money that will fund near-term consumpt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High dividends are attractive</td>\n",
       "      <td>High dividends are unattractive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Value stocks and small stocks are attractive.</td>\n",
       "      <td>Value stocks and small stocks may or may not b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hold international stocks, but far less than i...</td>\n",
       "      <td>Hold international stocks in proportion to the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Either prioritize paying highest interest debt...</td>\n",
       "      <td>Prioritize paying highest-interest debt.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Co-holding low-interest assets and high-intere...</td>\n",
       "      <td>Do not co-hold low-interest assets and high-in...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Choose a fixed-rate mortgage.</td>\n",
       "      <td>Choose an adjustable-rate mortgage unless inte...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Popular Books Advice  \\\n",
       "0  Save 10–15 percent of income regardless of age...   \n",
       "1  Don’t annuitize. Spend to keep real level of w...   \n",
       "2  Divide savings into mental accounts devoted to...   \n",
       "3  Hold money that might be spent in short term e...   \n",
       "4                      High dividends are attractive   \n",
       "5      Value stocks and small stocks are attractive.   \n",
       "6  Hold international stocks, but far less than i...   \n",
       "7  Either prioritize paying highest interest debt...   \n",
       "8  Co-holding low-interest assets and high-intere...   \n",
       "9                      Choose a fixed-rate mortgage.   \n",
       "\n",
       "                                        Profs Advice  \n",
       "0  Smooth consumption over time. Low or negative ...  \n",
       "1  Fully annuitize wealth in retirement. If not a...  \n",
       "2                            All wealth is fungible.  \n",
       "3  Invest money that will fund near-term consumpt...  \n",
       "4                    High dividends are unattractive  \n",
       "5  Value stocks and small stocks may or may not b...  \n",
       "6  Hold international stocks in proportion to the...  \n",
       "7           Prioritize paying highest-interest debt.  \n",
       "8  Do not co-hold low-interest assets and high-in...  \n",
       "9  Choose an adjustable-rate mortgage unless inte...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Choi 2022 claims\n",
    "claimchoipopular=['Save 10–15 percent of income regardless of age and circumstances during working life.'\n",
    ",'Don’t annuitize. Spend to keep real level of wealth roughly constant in retirement.'\n",
    ",'Divide savings into mental accounts devoted to different goals.'             \n",
    ",'Hold money that might be spent in short term entirely in cash. Money that won’t be spent in short term may be invested in equities.'\n",
    ",'High dividends are attractive'\n",
    ",'Value stocks and small stocks are attractive.'\n",
    ",'Hold international stocks, but far less than in proportion to their global market cap weight.'                  \n",
    ",'Either prioritize paying highest interest debt or lowest-balance debt.'\n",
    ",'Co-holding low-interest assets and high-interest debt may be a good idea.'                  \n",
    ",'Choose a fixed-rate mortgage.']                  \n",
    "                  \n",
    "                  \n",
    "claimchoiprof=['Smooth consumption over time. Low or negative savings rates when young, high savings rate in midlife.'\n",
    ",'Fully annuitize wealth in retirement. If not annuitized, negative savings rate in retirement.'\n",
    ",'All wealth is fungible.'               \n",
    ",'Invest money that will fund near-term consumption more conservatively than money that will fund consumption far in the future.'\n",
    ",'High dividends are unattractive'\n",
    ",'Value stocks and small stocks may or may not be attractive.'\n",
    ",'Hold international stocks in proportion to their global market cap weight.'\n",
    ",'Prioritize paying highest-interest debt.'\n",
    ",'Do not co-hold low-interest assets and high-interest debt.'\n",
    ",'Choose an adjustable-rate mortgage unless interest rates are low.'\n",
    "]\n",
    "\n",
    "\n",
    "import pandas as pd\n",
    "choi=pd.DataFrame()\n",
    "choi['Popular Books Advice']=claimchoipopular\n",
    "choi['Profs Advice']=claimchoiprof\n",
    "choi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a411306d-cc6b-4559-86e0-2ffb7ce187c3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "67\n",
      "68\n",
      "69\n",
      "70\n",
      "71\n",
      "72\n",
      "73\n",
      "74\n",
      "75\n",
      "76\n",
      "77\n",
      "78\n",
      "79\n",
      "80\n",
      "81\n",
      "82\n",
      "83\n",
      "84\n",
      "85\n",
      "86\n",
      "87\n",
      "88\n",
      "89\n",
      "90\n",
      "91\n",
      "92\n",
      "93\n",
      "94\n",
      "95\n",
      "96\n",
      "97\n",
      "98\n",
      "99\n"
     ]
    }
   ],
   "source": [
    "# Getting ChatGPT's opinion on claims of Choi\n",
    "from openai import OpenAI\n",
    "client = OpenAI(api_key=api_key)\n",
    "n=100\n",
    "# i do 100 but do 2 different orders below to see whether order matters\n",
    "claimanswer=[]\n",
    "for i in range(0,len(claimchoiprof)):\n",
    "    for j in range(0,n):\n",
    "        print(j)\n",
    "        content= 'which of the following two financial advice statements do you think is better:\\' ' + claimchoiprof[i] + '\\' or \\' ' + claimchoipopular[i] + '\\' answer with one of the two statements only'\n",
    "        \n",
    "        completion = client.chat.completions.create(\n",
    "                    model=\"gpt-4o\",\n",
    "                  messages=[\n",
    "                    {\"role\": \"user\", \"content\": content}\n",
    "                  ])\n",
    "            \n",
    "        #claimanswerlateststring=completion.choices[0].message.content\n",
    "        claimanswer+=[completion.choices[0].message.content]\n",
    "        \n",
    "        content= 'which of the following two financial advice statements do you think is better:\\' ' + claimchoipopular[i] + '\\' or \\' ' + claimchoiprof[i] + '\\' answer with one of the two statements only'\n",
    "        \n",
    "        completion = client.chat.completions.create(\n",
    "                    model=\"gpt-4o\",\n",
    "                  messages=[\n",
    "                    {\"role\": \"user\", \"content\": content}\n",
    "                  ])\n",
    "            \n",
    "        #claimanswerlateststring=completion.choices[0].message.content\n",
    "        claimanswer+=[completion.choices[0].message.content]\n",
    "\n",
    "# save info\n",
    "import pandas as pd\n",
    "df=pd.DataFrame()\n",
    "df['claimanswer']=claimanswer\n",
    "df.to_pickle('claimschoi090824')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d02da3ca-63a7-463c-a722-c1e0848ead7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  text-align: center;\n",
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       "#T_2b1c5 th {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_2b1c5 .true {\n",
       "  color: blue;\n",
       "}\n",
       "#T_2b1c5_row0_col0, #T_2b1c5_row0_col1, #T_2b1c5_row0_col2, #T_2b1c5_row0_col3, #T_2b1c5_row1_col0, #T_2b1c5_row1_col1, #T_2b1c5_row1_col2, #T_2b1c5_row1_col3, #T_2b1c5_row2_col0, #T_2b1c5_row2_col1, #T_2b1c5_row2_col2, #T_2b1c5_row2_col3, #T_2b1c5_row3_col0, #T_2b1c5_row3_col1, #T_2b1c5_row3_col2, #T_2b1c5_row3_col3, #T_2b1c5_row4_col0, #T_2b1c5_row4_col1, #T_2b1c5_row4_col2, #T_2b1c5_row4_col3, #T_2b1c5_row5_col0, #T_2b1c5_row5_col1, #T_2b1c5_row5_col2, #T_2b1c5_row5_col3, #T_2b1c5_row6_col0, #T_2b1c5_row6_col1, #T_2b1c5_row6_col2, #T_2b1c5_row6_col3, #T_2b1c5_row7_col0, #T_2b1c5_row7_col1, #T_2b1c5_row7_col2, #T_2b1c5_row7_col3, #T_2b1c5_row8_col0, #T_2b1c5_row8_col1, #T_2b1c5_row8_col2, #T_2b1c5_row8_col3, #T_2b1c5_row9_col0, #T_2b1c5_row9_col1, #T_2b1c5_row9_col2, #T_2b1c5_row9_col3 {\n",
       "  width: 300px;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_2b1c5\">\n",
       "  <caption>Table 9 - Does ChatGPT4o Prefers Popular Advice or Profs Advice - based on Choi (2022))</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_2b1c5_level0_col0\" class=\"col_heading level0 col0\" >Popular Books Advice</th>\n",
       "      <th id=\"T_2b1c5_level0_col1\" class=\"col_heading level0 col1\" >Profs Advice</th>\n",
       "      <th id=\"T_2b1c5_level0_col2\" class=\"col_heading level0 col2\" >Share Popular Books Advice</th>\n",
       "      <th id=\"T_2b1c5_level0_col3\" class=\"col_heading level0 col3\" >Share Profs Advice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row0_col0\" class=\"data row0 col0\" >Save 10–15 percent of income regardless of age and circumstances during working life.</td>\n",
       "      <td id=\"T_2b1c5_row0_col1\" class=\"data row0 col1\" >Smooth consumption over time. Low or negative savings rates when young, high savings rate in midlife.</td>\n",
       "      <td id=\"T_2b1c5_row0_col2\" class=\"data row0 col2\" >15.5</td>\n",
       "      <td id=\"T_2b1c5_row0_col3\" class=\"data row0 col3\" >84.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row1_col0\" class=\"data row1 col0\" >Don’t annuitize. Spend to keep real level of wealth roughly constant in retirement.</td>\n",
       "      <td id=\"T_2b1c5_row1_col1\" class=\"data row1 col1\" >Fully annuitize wealth in retirement. If not annuitized, negative savings rate in retirement.</td>\n",
       "      <td id=\"T_2b1c5_row1_col2\" class=\"data row1 col2\" >60.0</td>\n",
       "      <td id=\"T_2b1c5_row1_col3\" class=\"data row1 col3\" >40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row2_col0\" class=\"data row2 col0\" >Divide savings into mental accounts devoted to different goals.</td>\n",
       "      <td id=\"T_2b1c5_row2_col1\" class=\"data row2 col1\" >All wealth is fungible.</td>\n",
       "      <td id=\"T_2b1c5_row2_col2\" class=\"data row2 col2\" >83.0</td>\n",
       "      <td id=\"T_2b1c5_row2_col3\" class=\"data row2 col3\" >17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row3_col0\" class=\"data row3 col0\" >Hold money that might be spent in short term entirely in cash. Money that won’t be spent in short term may be invested in equities.</td>\n",
       "      <td id=\"T_2b1c5_row3_col1\" class=\"data row3 col1\" >Invest money that will fund near-term consumption more conservatively than money that will fund consumption far in the future.</td>\n",
       "      <td id=\"T_2b1c5_row3_col2\" class=\"data row3 col2\" >0.0</td>\n",
       "      <td id=\"T_2b1c5_row3_col3\" class=\"data row3 col3\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row4_col0\" class=\"data row4 col0\" >High dividends are attractive</td>\n",
       "      <td id=\"T_2b1c5_row4_col1\" class=\"data row4 col1\" >High dividends are unattractive</td>\n",
       "      <td id=\"T_2b1c5_row4_col2\" class=\"data row4 col2\" >94.0</td>\n",
       "      <td id=\"T_2b1c5_row4_col3\" class=\"data row4 col3\" >6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row5_col0\" class=\"data row5 col0\" >Value stocks and small stocks are attractive.</td>\n",
       "      <td id=\"T_2b1c5_row5_col1\" class=\"data row5 col1\" >Value stocks and small stocks may or may not be attractive.</td>\n",
       "      <td id=\"T_2b1c5_row5_col2\" class=\"data row5 col2\" >12.5</td>\n",
       "      <td id=\"T_2b1c5_row5_col3\" class=\"data row5 col3\" >87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row6_col0\" class=\"data row6 col0\" >Hold international stocks, but far less than in proportion to their global market cap weight.</td>\n",
       "      <td id=\"T_2b1c5_row6_col1\" class=\"data row6 col1\" >Hold international stocks in proportion to their global market cap weight.</td>\n",
       "      <td id=\"T_2b1c5_row6_col2\" class=\"data row6 col2\" >0.5</td>\n",
       "      <td id=\"T_2b1c5_row6_col3\" class=\"data row6 col3\" >99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row7_col0\" class=\"data row7 col0\" >Either prioritize paying highest interest debt or lowest-balance debt.</td>\n",
       "      <td id=\"T_2b1c5_row7_col1\" class=\"data row7 col1\" >Prioritize paying highest-interest debt.</td>\n",
       "      <td id=\"T_2b1c5_row7_col2\" class=\"data row7 col2\" >49.5</td>\n",
       "      <td id=\"T_2b1c5_row7_col3\" class=\"data row7 col3\" >50.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row8_col0\" class=\"data row8 col0\" >Co-holding low-interest assets and high-interest debt may be a good idea.</td>\n",
       "      <td id=\"T_2b1c5_row8_col1\" class=\"data row8 col1\" >Do not co-hold low-interest assets and high-interest debt.</td>\n",
       "      <td id=\"T_2b1c5_row8_col2\" class=\"data row8 col2\" >0.0</td>\n",
       "      <td id=\"T_2b1c5_row8_col3\" class=\"data row8 col3\" >100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2b1c5_row9_col0\" class=\"data row9 col0\" >Choose a fixed-rate mortgage.</td>\n",
       "      <td id=\"T_2b1c5_row9_col1\" class=\"data row9 col1\" >Choose an adjustable-rate mortgage unless interest rates are low.</td>\n",
       "      <td id=\"T_2b1c5_row9_col2\" class=\"data row9 col2\" >93.5</td>\n",
       "      <td id=\"T_2b1c5_row9_col3\" class=\"data row9 col3\" >6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fd967a66a30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Notes: this table is based on the statements in Choi (2022). \n",
      "It gives the chance ChatGPT4o choses a specific statement\n"
     ]
    }
   ],
   "source": [
    "# choianalysing Choi data\n",
    "# put it into dataframe\n",
    "import pandas as pd\n",
    "df=pd.read_pickle('claimschoi090824')\n",
    "choi=pd.DataFrame()\n",
    "choi['Popular Books Advice']=claimchoipopular\n",
    "choi['Profs Advice']=claimchoiprof\n",
    "choi\n",
    "choi['popularcounts']=None\n",
    "choi['profcounts']=None\n",
    "\n",
    "\n",
    "choi.at[0,'popularcounts']=sum(df['claimanswer'].str.contains('regardless', case=False))\n",
    "choi.at[0,'profcounts']=sum(df['claimanswer'].str.contains('smooth', case=False))\n",
    "choi.at[1,'popularcounts']=sum(df['claimanswer'].str.contains('annuitize. Spend', case=False))\n",
    "choi.at[1,'profcounts']=sum(df['claimanswer'].str.contains('Fully annuitize', case=False))\n",
    "choi.at[2,'popularcounts']=sum(df['claimanswer'].str.contains('mental accounts', case=False))\n",
    "choi.at[2,'profcounts']=sum(df['claimanswer'].str.contains('fungible', case=False))\n",
    "choi.at[3,'popularcounts']=sum(df['claimanswer'].str.contains('hold money', case=False))\n",
    "choi.at[3,'profcounts']=sum(df['claimanswer'].str.contains('invest money', case=False))\n",
    "choi.at[4,'popularcounts']=sum(df['claimanswer'].str.contains('dividends are attractive', case=False))\n",
    "choi.at[4,'profcounts']=sum(df['claimanswer'].str.contains('unattractive', case=False))\n",
    "choi.at[5,'popularcounts']=sum(df['claimanswer'].str.contains('stocks are attractive', case=False))\n",
    "choi.at[5,'profcounts']=sum(df['claimanswer'].str.contains('may or may not', case=False))\n",
    "choi.at[6,'popularcounts']=sum(df['claimanswer'].str.contains('far less', case=False))\n",
    "choi.at[6,'profcounts']=sum(df['claimanswer'].str.contains('stocks in proportion', case=False))\n",
    "choi.at[7,'popularcounts']=sum(df['claimanswer'].str.contains('either prioritize', case=False))\n",
    "choi.at[7,'profcounts']=sum(~df['claimanswer'].str.contains('either prioritize', case=False))-1800\n",
    "choi.at[8,'popularcounts']=sum(df['claimanswer'].str.contains('co-holding', case=False))\n",
    "choi.at[8,'profcounts']=sum(df['claimanswer'].str.contains('do not co-hold', case=False))\n",
    "choi.at[9,'popularcounts']=sum(df['claimanswer'].str.contains('fixed-rate', case=False))\n",
    "choi.at[9,'profcounts']=sum(df['claimanswer'].str.contains('adjustable', case=False))\n",
    "\n",
    "\n",
    "choi=choi[['Popular Books Advice','Profs Advice','popularcounts','profcounts']]\n",
    "\n",
    "# styles for tables\n",
    "styles = [{'selector': 'caption', 'props': 'text-align: center; font-size: 150%; color: blue'},\n",
    "        {'selector': 'td', 'props': 'text-align: center;'},\n",
    "         {'selector': 'th', 'props': 'text-align: center;'},  \n",
    "         {'selector': '.true', 'props': 'color: blue;'}]\n",
    "choi['popularcounts']=choi['popularcounts']/2\n",
    "choi['profcounts']=choi['profcounts']/2\n",
    "\n",
    "choi.columns=['Popular Books Advice', 'Profs Advice', 'Share Popular Books Advice', 'Share Profs Advice']\n",
    "#choi.style.set_caption(\"ChatGPT chooses financial advice from Economics Profs or popular textbooks?\").style.format(precision=1).set_table_styles(styles).hide(axis='index')\n",
    "display(choi.style.set_properties(**{'width': '300px'}).set_caption(\"Table 9 - Does ChatGPT4o Prefers Popular Advice or Profs Advice - based on Choi (2022))\").set_table_styles(styles).format(precision=1).hide(axis='index'))\n",
    "print('Notes: this table is based on the statements in Choi (2022). \\nIt gives the chance ChatGPT4o choses a specific statement')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3479bdd9-4380-41e9-b370-d2ff06c5a7f5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <td>Either prioritize paying highest interest debt...</td>\n",
       "      <td>Prioritize paying highest-interest debt.</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Co-holding low-interest assets and high-intere...</td>\n",
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       "      <th>9</th>\n",
       "      <td>Choose a fixed-rate mortgage.</td>\n",
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      ],
      "text/plain": [
       "                                Popular Books Advice  \\\n",
       "0  Save 10–15 percent of income regardless of age...   \n",
       "1  Don’t annuitize. Spend to keep real level of w...   \n",
       "2  Divide savings into mental accounts devoted to...   \n",
       "3  Hold money that might be spent in short term e...   \n",
       "4                      High dividends are attractive   \n",
       "5      Value stocks and small stocks are attractive.   \n",
       "6  Hold international stocks, but far less than i...   \n",
       "7  Either prioritize paying highest interest debt...   \n",
       "8  Co-holding low-interest assets and high-intere...   \n",
       "9                      Choose a fixed-rate mortgage.   \n",
       "\n",
       "                                        Profs Advice popularcounts profcounts  \n",
       "0  Smooth consumption over time. Low or negative ...            31         69  \n",
       "1  Fully annuitize wealth in retirement. If not a...           100          0  \n",
       "2                            All wealth is fungible.           100          0  \n",
       "3  Invest money that will fund near-term consumpt...             0        100  \n",
       "4                    High dividends are unattractive            95          5  \n",
       "5  Value stocks and small stocks may or may not b...            25         75  \n",
       "6  Hold international stocks in proportion to the...             1         99  \n",
       "7           Prioritize paying highest-interest debt.            99          1  \n",
       "8  Do not co-hold low-interest assets and high-in...             0        100  \n",
       "9  Choose an adjustable-rate mortgage unless inte...           100          0  "
      ]
     },
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Popular Books Advice</th>\n",
       "      <th>Profs Advice</th>\n",
       "      <th>popularcounts</th>\n",
       "      <th>profcounts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Save 10–15 percent of income regardless of age...</td>\n",
       "      <td>Smooth consumption over time. Low or negative ...</td>\n",
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Don’t annuitize. Spend to keep real level of w...</td>\n",
       "      <td>Fully annuitize wealth in retirement. If not a...</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divide savings into mental accounts devoted to...</td>\n",
       "      <td>All wealth is fungible.</td>\n",
       "      <td>66</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hold money that might be spent in short term e...</td>\n",
       "      <td>Invest money that will fund near-term consumpt...</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>High dividends are attractive</td>\n",
       "      <td>High dividends are unattractive</td>\n",
       "      <td>93</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Value stocks and small stocks are attractive.</td>\n",
       "      <td>Value stocks and small stocks may or may not b...</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hold international stocks, but far less than i...</td>\n",
       "      <td>Hold international stocks in proportion to the...</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Either prioritize paying highest interest debt...</td>\n",
       "      <td>Prioritize paying highest-interest debt.</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Co-holding low-interest assets and high-intere...</td>\n",
       "      <td>Do not co-hold low-interest assets and high-in...</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Choose a fixed-rate mortgage.</td>\n",
       "      <td>Choose an adjustable-rate mortgage unless inte...</td>\n",
       "      <td>87</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Popular Books Advice  \\\n",
       "0  Save 10–15 percent of income regardless of age...   \n",
       "1  Don’t annuitize. Spend to keep real level of w...   \n",
       "2  Divide savings into mental accounts devoted to...   \n",
       "3  Hold money that might be spent in short term e...   \n",
       "4                      High dividends are attractive   \n",
       "5      Value stocks and small stocks are attractive.   \n",
       "6  Hold international stocks, but far less than i...   \n",
       "7  Either prioritize paying highest interest debt...   \n",
       "8  Co-holding low-interest assets and high-intere...   \n",
       "9                      Choose a fixed-rate mortgage.   \n",
       "\n",
       "                                        Profs Advice popularcounts profcounts  \n",
       "0  Smooth consumption over time. Low or negative ...             0        100  \n",
       "1  Fully annuitize wealth in retirement. If not a...            20         80  \n",
       "2                            All wealth is fungible.            66         34  \n",
       "3  Invest money that will fund near-term consumpt...             0        100  \n",
       "4                    High dividends are unattractive            93          7  \n",
       "5  Value stocks and small stocks may or may not b...             0        100  \n",
       "6  Hold international stocks in proportion to the...             0        100  \n",
       "7           Prioritize paying highest-interest debt.             0        100  \n",
       "8  Do not co-hold low-interest assets and high-in...             0        100  \n",
       "9  Choose an adjustable-rate mortgage unless inte...            87         13  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# does order matter\n",
    "# choianalysing Choi data\n",
    "# put it into dataframe\n",
    "import pandas as pd\n",
    "df=pd.read_pickle('claimschoi090824')\n",
    "df=df[0:len(df):2].reset_index(drop=True)\n",
    "choi['popularcounts']=None\n",
    "choi['profcounts']=None\n",
    "\n",
    "\n",
    "choi.at[0,'popularcounts']=sum(df['claimanswer'].str.contains('regardless', case=False))\n",
    "choi.at[0,'profcounts']=sum(df['claimanswer'].str.contains('smooth', case=False))\n",
    "choi.at[1,'popularcounts']=sum(df['claimanswer'].str.contains('annuitize. Spend', case=False))\n",
    "choi.at[1,'profcounts']=sum(df['claimanswer'].str.contains('Fully annuitize', case=False))\n",
    "choi.at[2,'popularcounts']=sum(df['claimanswer'].str.contains('mental accounts', case=False))\n",
    "choi.at[2,'profcounts']=sum(df['claimanswer'].str.contains('fungible', case=False))\n",
    "choi.at[3,'popularcounts']=sum(df['claimanswer'].str.contains('hold money', case=False))\n",
    "choi.at[3,'profcounts']=sum(df['claimanswer'].str.contains('invest money', case=False))\n",
    "choi.at[4,'popularcounts']=sum(df['claimanswer'].str.contains('dividends are attractive', case=False))\n",
    "choi.at[4,'profcounts']=sum(df['claimanswer'].str.contains('unattractive', case=False))\n",
    "choi.at[5,'popularcounts']=sum(df['claimanswer'].str.contains('stocks are attractive', case=False))\n",
    "choi.at[5,'profcounts']=sum(df['claimanswer'].str.contains('may or may not', case=False))\n",
    "choi.at[6,'popularcounts']=sum(df['claimanswer'].str.contains('far less', case=False))\n",
    "choi.at[6,'profcounts']=sum(df['claimanswer'].str.contains('stocks in proportion', case=False))\n",
    "choi.at[7,'popularcounts']=sum(df['claimanswer'].str.contains('either prioritize', case=False))\n",
    "choi.at[7,'profcounts']=sum(~df['claimanswer'].str.contains('either prioritize', case=False))-900\n",
    "choi.at[8,'popularcounts']=sum(df['claimanswer'].str.contains('co-holding', case=False))\n",
    "choi.at[8,'profcounts']=sum(df['claimanswer'].str.contains('do not co-hold', case=False))\n",
    "choi.at[9,'popularcounts']=sum(df['claimanswer'].str.contains('fixed-rate', case=False))\n",
    "choi.at[9,'profcounts']=sum(df['claimanswer'].str.contains('adjustable', case=False))\n",
    "\n",
    "choi=choi[['Popular Books Advice','Profs Advice','popularcounts','profcounts']]\n",
    "display(choi)\n",
    "\n",
    "df=pd.read_pickle('claimschoi090824')\n",
    "df=df[1:len(df):2].reset_index(drop=True)\n",
    "choi['popularcounts']=None\n",
    "choi['profcounts']=None\n",
    "\n",
    "\n",
    "choi.at[0,'popularcounts']=sum(df['claimanswer'].str.contains('regardless', case=False))\n",
    "choi.at[0,'profcounts']=sum(df['claimanswer'].str.contains('smooth', case=False))\n",
    "choi.at[1,'popularcounts']=sum(df['claimanswer'].str.contains('annuitize. Spend', case=False))\n",
    "choi.at[1,'profcounts']=sum(df['claimanswer'].str.contains('Fully annuitize', case=False))\n",
    "choi.at[2,'popularcounts']=sum(df['claimanswer'].str.contains('mental accounts', case=False))\n",
    "choi.at[2,'profcounts']=sum(df['claimanswer'].str.contains('fungible', case=False))\n",
    "choi.at[3,'popularcounts']=sum(df['claimanswer'].str.contains('hold money', case=False))\n",
    "choi.at[3,'profcounts']=sum(df['claimanswer'].str.contains('invest money', case=False))\n",
    "choi.at[4,'popularcounts']=sum(df['claimanswer'].str.contains('dividends are attractive', case=False))\n",
    "choi.at[4,'profcounts']=sum(df['claimanswer'].str.contains('unattractive', case=False))\n",
    "choi.at[5,'popularcounts']=sum(df['claimanswer'].str.contains('stocks are attractive', case=False))\n",
    "choi.at[5,'profcounts']=sum(df['claimanswer'].str.contains('may or may not', case=False))\n",
    "choi.at[6,'popularcounts']=sum(df['claimanswer'].str.contains('far less', case=False))\n",
    "choi.at[6,'profcounts']=sum(df['claimanswer'].str.contains('stocks in proportion', case=False))\n",
    "choi.at[7,'popularcounts']=sum(df['claimanswer'].str.contains('either prioritize', case=False))\n",
    "choi.at[7,'profcounts']=sum(~df['claimanswer'].str.contains('either prioritize', case=False))-900\n",
    "choi.at[8,'popularcounts']=sum(df['claimanswer'].str.contains('co-holding', case=False))\n",
    "choi.at[8,'profcounts']=sum(df['claimanswer'].str.contains('do not co-hold', case=False))\n",
    "choi.at[9,'popularcounts']=sum(df['claimanswer'].str.contains('fixed-rate', case=False))\n",
    "choi.at[9,'profcounts']=sum(df['claimanswer'].str.contains('adjustable', case=False))\n",
    "\n",
    "choi=choi[['Popular Books Advice','Profs Advice','popularcounts','profcounts']]\n",
    "choi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e1b8c0-b393-48ee-9377-5cccfa1a3d66",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d066629-02da-49d0-9957-19d874289504",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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